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{"parentUuid":"c222664a-51fd-43ff-9b88-52e88e00c49c","isSidechain":false,"promptId":"ef574d05-d28e-41e2-b009-fce8ac301d89","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SCz3Rj3UNn9PVT5wenRfac","type":"tool_result","content":"# T1-forecast — Real-time forecasting of US retail-sales growth from data vintages: a complete mini-project\n\n## Scientific question\nHow well could the year-over-year growth of US retail and food services sales have been forecast *in real time* — using only the data actually published at each forecast origin — one and three months beyond the last published month, and does a model of your own design beat the no-change (naive) forecast once the forecast errors are measured against the first published value of each target month? This is an end-to-end forecasting project: question → data audit → real-time baselines → your model → rolling-origin evaluation → uncertainty → figure → short paper. The estimands are, for horizons h ∈ {1, 3} months and over the 10 forecast origins defined below: (a) the out-of-sample RMSE and MAE of three baselines (naive, seasonal naive, AR(1)) and of your model, all evaluated against the first-release value of the target; (b) the relative improvement of your model over the naive forecast, 1 − RMSE_model / RMSE_naive; (c) the Diebold–Mariano statistic of your model against the naive forecast under squared-error loss, with a long-run variance that is robust to the serial correlation of multi-step forecast errors; (d) the mean absolute revision between the first-release growth of the h = 1 target months and their currently published growth. Report what the data show, including a negative result if your model does not beat the naive forecast.\n\n## Inputs\nAll series are monthly, seasonally adjusted, dated by the first day of the month, and come from FRED/ALFRED (Federal Reserve Bank of St. Louis); full provenance, retrieval times (2026-10-30 UTC), SHA-256 and terms are in `data/README.md`.\n- `data/vintages/RSAFS_vintage_<YYYY-MM-DD>.csv` — 10 files, one per forecast origin (2025-10-31, 2025-11-30, 2025-12-31, 2026-01-31, 2026-02-28, 2026-03-31, 2026-04-30, 2026-05-31, 2026-06-30, 2026-07-31): the series RSAFS — Advance Retail Sales: Retail Trade and Food Services, total, millions of dollars, SA (US Census Bureau) — exactly as it was published on that date (ALFRED vintage; header `observation_date,RSAFS_<yyyymmdd>`; first observation 1992-01; the last row is the most recent month published at that date). The target variable is the year-over-year growth g_m = 100 · ln(L_m / L_{m−12}) in percent, where L is the level in the relevant vintage.\n- `data/vintages/PAYEMS_vintage_<YYYY-MM-DD>.csv` — 10 files, same dates: the series PAYEMS — All Employees, Total Nonfarm, thousands of persons, SA (US Bureau of Labor Statistics) — as published on that date (ALFRED vintage; first observation 1939-01). A candidate predictor.\n- `data/UMCSENT.csv` — the series UMCSENT — University of Michigan Index of Consumer Sentiment (1966Q1 = 100) — current vintage as served by FRED on 2026-10-30 (header `observation_date,UMCSENT`; 1952-11 to 2026-09; the field is empty for the months before 1978 in which the survey was not conducted). A candidate predictor; see the availability rule below.\n- `data/RSAFS_current.csv` — RSAFS as served by FRED on 2026-10-30 (header `observation_date,RSAFS`; 1992-01 to 2026-09), containing all revisions known today. **Provided only for the revision analysis (estimand (d)); it may not enter any forecast.**\n- `data/first_release.csv` — one row per target month from 2024-06 to 2026-09: `target_month`, `first_release_vintage` (the first month-end ALFRED vintage in the designer's grid 2024-06-30 … 2026-10-29 in which the month appears), `level_first_release` (the RSAFS level of the month in that vintage), `level_prev_year_same_vintage` (the level of the month twelve months earlier in the same vintage) and `yoy_first_release` = 100 · ln(level_first_release / level_prev_year_same_vintage), 6 decimals. This is the \"actual\" against which every forecast is evaluated.\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- Forecast origins are the 10 vintage dates listed above. A forecast made at origin V may use **only** the RSAFS vintage file dated V, the PAYEMS vintage file dated V, and the UMCSENT values of months on or before the month of V (the final sentiment reading of a month is published before the end of that month and is not revised afterwards; the current UMCSENT file is used with this rule). Estimation samples, model selection, transformations and the values plugged into a forecast must all respect this rule at every origin.\n- Let last(V) be the most recent month present in the RSAFS vintage dated V (it is not always the month before V: publication delays occurred). The target of origin V at horizon h is the month last(V) + h; the actual is `yoy_first_release` of that month in `data/first_release.csv`.\n- Baselines (all three required, computed from the RSAFS vintage dated V):\n  - **naive**: the forecast for both horizons is g_{last(V)}, the last available growth value in the vintage;\n  - **seasonal naive**: the forecast for target month T is g_{T−12} from the vintage;\n  - **AR(1)**: ordinary least squares of g_m on a constant and g_{m−1} over all consecutive pairs with 1993-02 ≤ m ≤ last(V) in the vintage, then iterated (plug-in) forecasts h steps beyond last(V).\n- Your model is free (it may use the vintage dynamics, PAYEMS and/or UMCSENT), but it must obey the availability rule and must be described in `model_description`.\n- Diebold–Mariano test (model vs naive, per horizon): loss differential d_t = e²_naive,t − e²_model,t over the 10 origins; `dm_stat_h` = d̄ / sqrt(σ̂²_LR / n) where σ̂²_LR is the Bartlett-kernel (Newey–West) long-run variance of d_t with truncation lag h − 1 (lag 0 only at h = 1; lags 1 and 2 with weights 2/3 and 1/3 at h = 3), without the Harvey–Leybourne–Newbold small-sample factor; `dm_pvalue_h` is the two-sided p-value from the standard normal. You may add other uncertainty measures (e.g. a bootstrap interval for the RMSE difference) in the report.\n- The series `RSAFS_current.csv` is used only for estimand (d): mean over the 10 h = 1 target months of |g^current_T − yoy_first_release_T|, where g^current is the growth computed from the current file.\n\n## Required outputs\n- `out/forecasts.csv` — exactly one row per (origin, horizon, model): 10 origins × 2 horizons × 4 models = 80 rows, columns `origin_date` (vintage date, YYYY-MM-DD), `target_date` (YYYY-MM-01), `horizon` (1 or 3), `forecast` (percent), `actual_first_release` (percent, from `first_release.csv`), `model` (one of `naive`, `snaive`, `ar1`, `model`), `vintage_used` (the vintage date of the RSAFS file used, YYYY-MM-DD).\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"T1-forecast\" |\n| `model_name` | string | — | short name of your model |\n| `model_description` | string | — | specification, predictors, estimation window, alignment of predictors with the availability rule |\n| `n_origins` | integer | — | 10 |\n| `first_origin`, `last_origin` | string | — | first and last origin dates (YYYY-MM-DD) |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percent | naive baseline errors against first-release actuals |\n| `rmse_snaive_h1`, `mae_snaive_h1`, `rmse_snaive_h3`, `mae_snaive_h3` | number | percent | seasonal-naive baseline errors |\n| `rmse_ar1_h1`, `mae_ar1_h1`, `rmse_ar1_h3`, `mae_ar1_h3` | number | percent | AR(1) baseline errors |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percent | your model's errors |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | — | 1 − RMSE_model / RMSE_naive |\n| `dm_stat_h1`, `dm_pvalue_h1`, `dm_stat_h3`, `dm_pvalue_h3` | number | — | Diebold–Mariano statistic and two-sided p-value, model vs naive, as defined above |\n| `conclusion_code_h1`, `conclusion_code_h3` | string | — | one of `model_better_significant`, `model_better_not_significant`, `naive_better_not_significant`, `naive_better_significant` (\"better\" = lower RMSE; \"significant\" = `dm_pvalue_h` < 0.05) |\n| `mean_abs_revision_h1_targets` | number | percent | estimand (d) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected (irregular publication timing, missing values, revisions that affect the design, …) and how it was handled; empty list if none |\n| `n_input_rows` | object | rows | {file name: number of data rows (lines after the header)} for every file under `data/` that you read |\n| `n_analysed` | integer or null | — | number of forecast–actual pairs evaluated for your model (both horizons) |\n| `random_seed` | integer or null | — | seed used, if any (e.g. bootstrap) |\n\n- `out/table1.csv` — the RMSE/MAE table: one row per (model, horizon) with columns `model`, `horizon`, `rmse`, `mae`, `n`.\n- `out/figure1.png` — first-release actuals and the forecasts of the naive baseline and of your model over the target months, one panel per horizon (or an equivalent single figure).\n- `out/report.md` — a short paper with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The \"Data and exclusions\" section must document the real-time design (which vintage feeds which forecast, publication delays observed) and the \"Uncertainty\" section must interpret the Diebold–Mariano results given the small number of origins.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/forecasts.csv`, `out/table1.csv` and `out/figure1.png` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Budget class: transfer mini-project — 12.00 USD cap, 60 minutes wall, 150 turns per session. The project is designed to be completed in well under 45 minutes of work.\nfirst_release.csv\nREADME.md\nRSAFS_current.csv\nUMCSENT.csv\nvintages\n\ndata/vintages:\nPAYEMS_vintage_2025-10-31.csv\nPAYEMS_vintage_2025-11-30.csv\nPAYEMS_vintage_2025-12-31.csv\nPAYEMS_vintage_2026-01-31.csv\nPAYEMS_vintage_2026-02-28.csv\nPAYEMS_vintage_2026-03-31.csv\nPAYEMS_vintage_2026-04-30.csv\nPAYEMS_vintage_2026-05-31.csv\nPAYEMS_vintage_2026-06-30.csv\nPAYEMS_vintage_2026-07-31.csv\nRSAFS_vintage_2025-10-31.csv\nRSAFS_vintage_2025-11-30.csv\nRSAFS_vintage_2025-12-31.csv\nRSAFS_vintage_2026-01-31.csv\nRSAFS_vintage_2026-02-28.csv\nRSAFS_vintage_2026-03-31.csv\nRSAFS_vintage_2026-04-30.csv\nRSAFS_vintage_2026-05-31.csv\nRSAFS_vintage_2026-06-30.csv\nRSAFS_vintage_2026-07-31.csv","is_error":false}]},"uuid":"94b3c951-4061-4efb-8c05-aed73d197b47","timestamp":"2026-10-30T05:33:39.646Z","toolUseResult":{"stdout":"# T1-forecast — Real-time forecasting of US retail-sales growth from data vintages: a complete mini-project\n\n## Scientific question\nHow well could the year-over-year growth of US retail and food services sales have been forecast *in real time* — using only the data actually published at each forecast origin — one and three months beyond the last published month, and does a model of your own design beat the no-change (naive) forecast once the forecast errors are measured against the first published value of each target month? This is an end-to-end forecasting project: question → data audit → real-time baselines → your model → rolling-origin evaluation → uncertainty → figure → short paper. The estimands are, for horizons h ∈ {1, 3} months and over the 10 forecast origins defined below: (a) the out-of-sample RMSE and MAE of three baselines (naive, seasonal naive, AR(1)) and of your model, all evaluated against the first-release value of the target; (b) the relative improvement of your model over the naive forecast, 1 − RMSE_model / RMSE_naive; (c) the Diebold–Mariano statistic of your model against the naive forecast under squared-error loss, with a long-run variance that is robust to the serial correlation of multi-step forecast errors; (d) the mean absolute revision between the first-release growth of the h = 1 target months and their currently published growth. Report what the data show, including a negative result if your model does not beat the naive forecast.\n\n## Inputs\nAll series are monthly, seasonally adjusted, dated by the first day of the month, and come from FRED/ALFRED (Federal Reserve Bank of St. Louis); full provenance, retrieval times (2026-10-30 UTC), SHA-256 and terms are in `data/README.md`.\n- `data/vintages/RSAFS_vintage_<YYYY-MM-DD>.csv` — 10 files, one per forecast origin (2025-10-31, 2025-11-30, 2025-12-31, 2026-01-31, 2026-02-28, 2026-03-31, 2026-04-30, 2026-05-31, 2026-06-30, 2026-07-31): the series RSAFS — Advance Retail Sales: Retail Trade and Food Services, total, millions of dollars, SA (US Census Bureau) — exactly as it was published on that date (ALFRED vintage; header `observation_date,RSAFS_<yyyymmdd>`; first observation 1992-01; the last row is the most recent month published at that date). The target variable is the year-over-year growth g_m = 100 · ln(L_m / L_{m−12}) in percent, where L is the level in the relevant vintage.\n- `data/vintages/PAYEMS_vintage_<YYYY-MM-DD>.csv` — 10 files, same dates: the series PAYEMS — All Employees, Total Nonfarm, thousands of persons, SA (US Bureau of Labor Statistics) — as published on that date (ALFRED vintage; first observation 1939-01). A candidate predictor.\n- `data/UMCSENT.csv` — the series UMCSENT — University of Michigan Index of Consumer Sentiment (1966Q1 = 100) — current vintage as served by FRED on 2026-10-30 (header `observation_date,UMCSENT`; 1952-11 to 2026-09; the field is empty for the months before 1978 in which the survey was not conducted). A candidate predictor; see the availability rule below.\n- `data/RSAFS_current.csv` — RSAFS as served by FRED on 2026-10-30 (header `observation_date,RSAFS`; 1992-01 to 2026-09), containing all revisions known today. **Provided only for the revision analysis (estimand (d)); it may not enter any forecast.**\n- `data/first_release.csv` — one row per target month from 2024-06 to 2026-09: `target_month`, `first_release_vintage` (the first month-end ALFRED vintage in the designer's grid 2024-06-30 … 2026-10-29 in which the month appears), `level_first_release` (the RSAFS level of the month in that vintage), `level_prev_year_same_vintage` (the level of the month twelve months earlier in the same vintage) and `yoy_first_release` = 100 · ln(level_first_release / level_prev_year_same_vintage), 6 decimals. This is the \"actual\" against which every forecast is evaluated.\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- Forecast origins are the 10 vintage dates listed above. A forecast made at origin V may use **only** the RSAFS vintage file dated V, the PAYEMS vintage file dated V, and the UMCSENT values of months on or before the month of V (the final sentiment reading of a month is published before the end of that month and is not revised afterwards; the current UMCSENT file is used with this rule). Estimation samples, model selection, transformations and the values plugged into a forecast must all respect this rule at every origin.\n- Let last(V) be the most recent month present in the RSAFS vintage dated V (it is not always the month before V: publication delays occurred). The target of origin V at horizon h is the month last(V) + h; the actual is `yoy_first_release` of that month in `data/first_release.csv`.\n- Baselines (all three required, computed from the RSAFS vintage dated V):\n  - **naive**: the forecast for both horizons is g_{last(V)}, the last available growth value in the vintage;\n  - **seasonal naive**: the forecast for target month T is g_{T−12} from the vintage;\n  - **AR(1)**: ordinary least squares of g_m on a constant and g_{m−1} over all consecutive pairs with 1993-02 ≤ m ≤ last(V) in the vintage, then iterated (plug-in) forecasts h steps beyond last(V).\n- Your model is free (it may use the vintage dynamics, PAYEMS and/or UMCSENT), but it must obey the availability rule and must be described in `model_description`.\n- Diebold–Mariano test (model vs naive, per horizon): loss differential d_t = e²_naive,t − e²_model,t over the 10 origins; `dm_stat_h` = d̄ / sqrt(σ̂²_LR / n) where σ̂²_LR is the Bartlett-kernel (Newey–West) long-run variance of d_t with truncation lag h − 1 (lag 0 only at h = 1; lags 1 and 2 with weights 2/3 and 1/3 at h = 3), without the Harvey–Leybourne–Newbold small-sample factor; `dm_pvalue_h` is the two-sided p-value from the standard normal. You may add other uncertainty measures (e.g. a bootstrap interval for the RMSE difference) in the report.\n- The series `RSAFS_current.csv` is used only for estimand (d): mean over the 10 h = 1 target months of |g^current_T − yoy_first_release_T|, where g^current is the growth computed from the current file.\n\n## Required outputs\n- `out/forecasts.csv` — exactly one row per (origin, horizon, model): 10 origins × 2 horizons × 4 models = 80 rows, columns `origin_date` (vintage date, YYYY-MM-DD), `target_date` (YYYY-MM-01), `horizon` (1 or 3), `forecast` (percent), `actual_first_release` (percent, from `first_release.csv`), `model` (one of `naive`, `snaive`, `ar1`, `model`), `vintage_used` (the vintage date of the RSAFS file used, YYYY-MM-DD).\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"T1-forecast\" |\n| `model_name` | string | — | short name of your model |\n| `model_description` | string | — | specification, predictors, estimation window, alignment of predictors with the availability rule |\n| `n_origins` | integer | — | 10 |\n| `first_origin`, `last_origin` | string | — | first and last origin dates (YYYY-MM-DD) |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percent | naive baseline errors against first-release actuals |\n| `rmse_snaive_h1`, `mae_snaive_h1`, `rmse_snaive_h3`, `mae_snaive_h3` | number | percent | seasonal-naive baseline errors |\n| `rmse_ar1_h1`, `mae_ar1_h1`, `rmse_ar1_h3`, `mae_ar1_h3` | number | percent | AR(1) baseline errors |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percent | your model's errors |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | — | 1 − RMSE_model / RMSE_naive |\n| `dm_stat_h1`, `dm_pvalue_h1`, `dm_stat_h3`, `dm_pvalue_h3` | number | — | Diebold–Mariano statistic and two-sided p-value, model vs naive, as defined above |\n| `conclusion_code_h1`, `conclusion_code_h3` | string | — | one of `model_better_significant`, `model_better_not_significant`, `naive_better_not_significant`, `naive_better_significant` (\"better\" = lower RMSE; \"significant\" = `dm_pvalue_h` < 0.05) |\n| `mean_abs_revision_h1_targets` | number | percent | estimand (d) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected (irregular publication timing, missing values, revisions that affect the design, …) and how it was handled; empty list if none |\n| `n_input_rows` | object | rows | {file name: number of data rows (lines after the header)} for every file under `data/` that you read |\n| `n_analysed` | integer or null | — | number of forecast–actual pairs evaluated for your model (both horizons) |\n| `random_seed` | integer or null | — | seed used, if any (e.g. bootstrap) |\n\n- `out/table1.csv` — the RMSE/MAE table: one row per (model, horizon) with columns `model`, `horizon`, `rmse`, `mae`, `n`.\n- `out/figure1.png` — first-release actuals and the forecasts of the naive baseline and of your model over the target months, one panel per horizon (or an equivalent single figure).\n- `out/report.md` — a short paper with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The \"Data and exclusions\" section must document the real-time design (which vintage feeds which forecast, publication delays observed) and the \"Uncertainty\" section must interpret the Diebold–Mariano results given the small number of origins.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/forecasts.csv`, `out/table1.csv` and `out/figure1.png` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Budget class: transfer mini-project — 12.00 USD cap, 60 minutes wall, 150 turns per session. 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{"parentUuid":"15a18081-530e-4555-be2a-27896b74de45","isSidechain":false,"promptId":"ef574d05-d28e-41e2-b009-fce8ac301d89","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015CagmrwGTs6oW32wPTnCPj","type":"tool_result","content":"# Data provenance — T1-forecast\n\nAll observed series were retrieved by the task designer on 2026-10-30 (between 04:00 and 04:01 UTC) from the Federal Reserve Bank of St. Louis FRED and ALFRED services, exactly as served (CSV, one header line, `observation_date` in YYYY-MM-DD, empty value field where the source reports no value). No value was altered. Row counts are data rows (lines after the header).\n\n| file | content | URL | retrieved (UTC) | data rows | SHA-256 |\n|---|---|---|---|---|---|\n| `vintages/PAYEMS_vintage_2025-10-31.csv` | PAYEMS as published on 2025-10-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-10-31` | 2026-10-30T04:00Z | 1040 | d4b4c909ed321fbf8021c385beb1512ac000009e22bb20bc27c791a248d33c60 |\n| `vintages/PAYEMS_vintage_2025-11-30.csv` | PAYEMS as published on 2025-11-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-11-30` | 2026-10-30T04:00Z | 1041 | a05ef7fc50cfc339207788f9d4205a619717bc44a335488d7790ae823bdb7b77 |\n| `vintages/PAYEMS_vintage_2025-12-31.csv` | PAYEMS as published on 2025-12-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-12-31` | 2026-10-30T04:00Z | 1043 | 829dae087af61916e87994220a3e5ebf5853fa43cc163b3798715603ec8bb031 |\n| `vintages/PAYEMS_vintage_2026-01-31.csv` | PAYEMS as published on 2026-01-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-01-31` | 2026-10-30T04:00Z | 1044 | 8fd01c5d2740e869458721a66b81bb2e247085e1e60cb93f3cb06bedf01885d7 |\n| `vintages/PAYEMS_vintage_2026-02-28.csv` | PAYEMS as published on 2026-02-28 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-02-28` | 2026-10-30T04:00Z | 1045 | 4a913e67ac5ffbb4d127f03f6f10ce0647998779629579617dabf1344d072db3 |\n| `vintages/PAYEMS_vintage_2026-03-31.csv` | PAYEMS as published on 2026-03-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-03-31` | 2026-10-30T04:00Z | 1046 | 919de86b07be90466e19844b54482ef8a3f6c8da7f9fe2a3ef3af0d02923c92e |\n| `vintages/PAYEMS_vintage_2026-04-30.csv` | PAYEMS as published on 2026-04-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-04-30` | 2026-10-30T04:00Z | 1047 | 06a8d663d9aaa61c6d5bda15d2893b10fd127b1d64fa16d47335341f62431c7a |\n| `vintages/PAYEMS_vintage_2026-05-31.csv` | PAYEMS as published on 2026-05-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-05-31` | 2026-10-30T04:00Z | 1048 | e2a152e874139acdebfc1715ffff9c1e5320f639f1443fff976b389724a5932d |\n| `vintages/PAYEMS_vintage_2026-06-30.csv` | PAYEMS as published on 2026-06-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-06-30` | 2026-10-30T04:00Z | 1049 | 19fbc6cd3547ffad1e22f32ac76f91758859804654b77622a4f42eac2d4e286a |\n| `vintages/PAYEMS_vintage_2026-07-31.csv` | PAYEMS as published on 2026-07-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-07-31` | 2026-10-30T04:00Z | 1050 | 3a3a6603fcc31c6eb2ee3293143888ab3440bbe3a962565eb39711b92019df41 |\n| `vintages/RSAFS_vintage_2025-10-31.csv` | RSAFS as published on 2025-10-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-10-31` | 2026-10-30T04:00Z | 404 | 3ecb0832a391e7261145af41a7a951f823df3a08d476162721022a0922d37e36 |\n| `vintages/RSAFS_vintage_2025-11-30.csv` | RSAFS as published on 2025-11-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-11-30` | 2026-10-30T04:00Z | 405 | 4a683d85374690f1e73d66c4f890156d0d32cd8b27d5d67b72582b9a0dd825a4 |\n| `vintages/RSAFS_vintage_2025-12-31.csv` | RSAFS as published on 2025-12-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-12-31` | 2026-10-30T04:00Z | 406 | 436df4b5509b1dfe61ea11b8f98dce27f012ff6b0255eb194eea564ad7e880dd |\n| `vintages/RSAFS_vintage_2026-01-31.csv` | RSAFS as published on 2026-01-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-01-31` | 2026-10-30T04:00Z | 407 | b92ab6a5c1dbdc5653e85c56fdb7dc0646f1fca7227ed6e64f1424e17446aa9e |\n| `vintages/RSAFS_vintage_2026-02-28.csv` | RSAFS as published on 2026-02-28 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-02-28` | 2026-10-30T04:00Z | 408 | 2af0a3aaeeba81efddb2613d326e3f96b5a1c88c41eb30ad384ab3cc4b289a77 |\n| `vintages/RSAFS_vintage_2026-03-31.csv` | RSAFS as published on 2026-03-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-03-31` | 2026-10-30T04:00Z | 409 | 0316fdf238d55d9f109b0be85e8c2d2eb00aee57016c0328d39405b349a8815f |\n| `vintages/RSAFS_vintage_2026-04-30.csv` | RSAFS as published on 2026-04-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-04-30` | 2026-10-30T04:00Z | 411 | 02dcc697a1db34753194974b1481dbd6a3ffcfe333864850e5ddd0e3202d2b8f |\n| `vintages/RSAFS_vintage_2026-05-31.csv` | RSAFS as published on 2026-05-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-05-31` | 2026-10-30T04:00Z | 412 | 8c3fe3530213589635ad6bb086f78ed613c459bf435c4558a5c51c597e19d87b |\n| `vintages/RSAFS_vintage_2026-06-30.csv` | RSAFS as published on 2026-06-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-06-30` | 2026-10-30T04:00Z | 413 | 2f103157f4a9b7a3c90c63af2033b9570d5e334205878714bbd35c8b304d7524 |\n| `vintages/RSAFS_vintage_2026-07-31.csv` | RSAFS as published on 2026-07-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-07-31` | 2026-10-30T04:00Z | 414 | 52c04a8fce2effa727f3ef267eaee04e13aef20f8aaabc982277d08fc5b8e5b2 |\n| `RSAFS_current.csv` | RSAFS (current vintage, all revisions to date) | `https://fred.stlouisfed.org/graph/fredgraph.csv?id=RSAFS` | 2026-10-30T04:00Z | 417 | 627d8bb386ac80f4aede3a9496b63040df584875fb766d455b08ebd84311468f |\n| `UMCSENT.csv` | UMCSENT (current vintage) | `https://fred.stlouisfed.org/graph/fredgraph.csv?id=UMCSENT` | 2026-10-30T04:00Z | 887 | 3076277f9f21f4bd3dc77fd68add84d417e5cce23f3adb96dc70313b3ad2dd46 |\n| `first_release.csv` | derived by the task designer from the month-end ALFRED vintages of RSAFS 2024-06-30 … 2026-10-29 (29 vintages; the 19 not shipped here were used only to date the first release of each month) | (derived) | 2026-10-30T04:00Z | 28 | 1d1ae8b3cacf7799aaefc7e0a969b3886e7979c36fce02ccf77d7c79fb7343e4 |\n\n## Series\n- **RSAFS** — Advance Retail Sales: Retail Trade and Food Services, Total. Source: U.S. Census Bureau, Advance Monthly Sales for Retail and Food Services (via FRED). Units: millions of dollars, seasonally adjusted, monthly, 1992-01 onward. The advance estimate of a month is normally published in the middle of the following month and is revised in the two following monthly releases and in annual revisions; a month-end ALFRED vintage therefore normally contains the months up to the month before the vintage date. The vintage grid in this bundle shows departures from this pattern (e.g. the vintages of 2025-10-31 and 2026-03-31 end two months before their date; the vintage of 2026-04-30 adds two months at once), which reflect the actual publication calendar; the task rules refer to the last month present in each vintage, not to the calendar.\n- **PAYEMS** — All Employees, Total Nonfarm. Source: U.S. Bureau of Labor Statistics, Current Employment Statistics (via FRED). Units: thousands of persons, seasonally adjusted, monthly, 1939-01 onward. Published in the Employment Situation release on the first Friday (normally) of the following month and revised in the two following releases and in annual benchmark revisions; the ALFRED vintages dated as the RSAFS vintages give the data available at each origin.\n- **UMCSENT** — University of Michigan: Consumer Sentiment, index 1966Q1 = 100, monthly (quarterly before 1978; empty fields where no survey was conducted). Source: University of Michigan, Surveys of Consumers (via FRED). The preliminary reading of a month is published in the middle of the month and the final reading at the end of the month; the series is not revised afterwards. The current FRED vintage is provided; at a month-end origin the final reading of the origin month is available (verified on the ALFRED vintages of 2025-10-31, 2026-03-31 and 2026-07-31, which contain the origin month).\n- **first_release.csv** — the first month-end ALFRED vintage in which each target month appears, the level printed there, the level of the month twelve months earlier in the same vintage, and the year-over-year growth 100·ln(ratio) (6 decimals). \"First release\" is defined at month-end granularity: for RSAFS the advance estimate is the only release of a month that can appear in the month-end vintage of the following month, so the value equals the advance estimate except where the publication calendar was disrupted (then it is the first published value as of the first month-end on which it existed).\n\n## Terms\nFRED/ALFRED data are provided by the Federal Reserve Bank of St. Louis under its terms of use (https://fred.stlouisfed.org/legal/); the underlying series are U.S. government statistics (Census Bureau, BLS: public domain) and the University of Michigan Surveys of Consumers index (redistributed by FRED with permission; cite \"University of Michigan, University of Michigan: Consumer Sentiment [UMCSENT], retrieved from FRED\"). The data are reproduced here unchanged for research use.\ntarget_month,first_release_vintage,level_first_release,level_prev_year_same_vintage,yoy_first_release\r\n2024-06-01,2024-07-31,704324,688630,2.253436\r\n2024-07-01,2024-08-31,709668,691250,2.62957\r\n2024-08-01,2024-09-30,710773,695940,2.108966\r\n2024-09-01,2024-10-31,714358,702160,1.722294\r\n2024-10-01,2024-11-30,718867,698956,2.808857\r\n2024-11-01,2024-12-31,724609,698105,3.726268\r\n2024-12-01,2025-01-31,729191,701672,3.846964\r\n2025-01-01,2025-02-28,723853,694696,4.111399\r\n2025-02-01,2025-03-31,722708,700932,3.059439\r\n2025-03-01,2025-04-30,722025,688248,4.791053\r\n2025-04-01,2025-05-31,724131,688629,5.026965\r\n2025-05-01,2025-06-30,715417,692635,3.236242\r\n2025-06-01,2025-07-31,720106,692922,3.848098\r\n2025-07-01,2025-08-31,726283,698909,3.84192\r\n2025-08-01,2025-09-30,732010,697157,4.878354\r\n2025-09-01,2025-11-30,733258,703285,4.17354\r\n2025-10-01,2025-12-31,732633,708070,3.410194\r\n2025-11-01,2026-01-31,735904,712175,3.277601\r\n2025-12-01,2026-02-28,734967,717547,2.398715\r\n2026-01-01,2026-03-31,733537,711095,3.1072\r\n2026-02-01,2026-04-30,739772,711575,3.886121\r\n2026-03-01,2026-04-30,752063,723350,3.89269\r\n2026-04-01,2026-05-31,757085,721903,4.758475\r\n2026-05-01,2026-06-30,763705,714568,6.650342\r\n2026-06-01,2026-07-31,768553,720164,6.503056\r\n2026-07-01,2026-08-31,763602,727176,4.887817\r\n2026-08-01,2026-09-30,737763,700218,5.223092\r\n2026-09-01,2026-10-29,745021,700948,6.09787\r\nvintages/PAYEMS_vintage_2025-10-31.csv 1041 observation_date,PAYEMS_20251031\n2025-06-01,159439\n2025-07-01,159518\n2025-08-01,159540\nvintages/PAYEMS_vintage_2025-11-30.csv 1042 observation_date,PAYEMS_20251130\n2025-07-01,159511\n2025-08-01,159507\n2025-09-01,159626\nvintages/PAYEMS_vintage_2025-12-31.csv 1044 observation_date,PAYEMS_20251231\n2025-09-01,159593\n2025-10-01,159488\n2025-11-01,159552\nvintages/PAYEMS_vintage_2026-01-31.csv 1045 observation_date,PAYEMS_20260131\n2025-10-01,159420\n2025-11-01,159476\n2025-12-01,159526\nvintages/PAYEMS_vintage_2026-02-28.csv 1046 observation_date,PAYEMS_20260228\n2025-11-01,158449\n2025-12-01,158497\n2026-01-01,158627\nvintages/PAYEMS_vintage_2026-03-31.csv 1047 observation_date,PAYEMS_20260331\n2025-12-01,158432\n2026-01-01,158558\n2026-02-01,158466\nvintages/PAYEMS_vintage_2026-04-30.csv 1048 observation_date,PAYEMS_20260430\n2026-01-01,158592\n2026-02-01,158459\n2026-03-01,158637\nvintages/PAYEMS_vintage_2026-05-31.csv 1049 observation_date,PAYEMS_20260531\n2026-02-01,158436\n2026-03-01,158621\n2026-04-01,158736\nvintages/PAYEMS_vintage_2026-06-30.csv 1050 observation_date,PAYEMS_20260630\n2026-03-01,158650\n2026-04-01,158829\n2026-05-01,159001\nvintages/PAYEMS_vintage_2026-07-31.csv 1051 observation_date,PAYEMS_20260731\n2026-04-01,158798\n2026-05-01,158927\n2026-06-01,158984\nvintages/RSAFS_vintage_2025-10-31.csv 405 observation_date,RSAFS_20251031\n2025-06-01,723033\n2025-07-01,727414\n2025-08-01,732010\nvintages/RSAFS_vintage_2025-11-30.csv 406 observation_date,RSAFS_20251130\n2025-07-01,727727\n2025-08-01,732059\n2025-09-01,733258\nvintages/RSAFS_vintage_2025-12-31.csv 407 observation_date,RSAFS_20251231\n2025-08-01,731700\n2025-09-01,732444\n2025-10-01,732633\nvintages/RSAFS_vintage_2026-01-31.csv 408 observation_date,RSAFS_20260131\n2025-09-01,732192\n2025-10-01,731412\n2025-11-01,735904\nvintages/RSAFS_vintage_2026-02-28.csv 409 observation_date,RSAFS_20260228\n2025-10-01,731051\n2025-11-01,735085\n2025-12-01,734967\nvintages/RSAFS_vintage_2026-03-31.csv 410 observation_date,RSAFS_20260331\n2025-11-01,734718\n2025-12-01,734685\n2026-01-01,733537\nvintages/RSAFS_vintage_2026-04-30.csv 412 observation_date,RSAFS_20260430\n2026-01-01,734503\n2026-02-01,739772\n2026-03-01,752063\nvintages/RSAFS_vintage_2026-05-31.csv 413 observation_date,RSAFS_20260531\n2026-02-01,741278\n2026-03-01,753370\n2026-04-01,757085\nvintages/RSAFS_vintage_2026-06-30.csv 414 observation_date,RSAFS_20260630\n2026-03-01,754013\n2026-04-01,757036\n2026-05-01,763705\nvintages/RSAFS_vintage_2026-07-31.csv 415 observation_date,RSAFS_20260731\n2026-04-01,759097\n2026-05-01,766876\n2026-06-01,768553\n2026-05-01,44.8\n2026-06-01,49.5\n2026-07-01,55.2\n2026-08-01,51.7\n2026-09-01,48.1\n2026-07-01,729915\n2026-08-01,739322\n2026-09-01,745021\nvintages/PAYEMS_vintage_2025-10-31.csv:0\nvintages/PAYEMS_vintage_2025-11-30.csv:0\nvintages/PAYEMS_vintage_2025-12-31.csv:0\nvintages/PAYEMS_vintage_2026-01-31.csv:0\nvintages/PAYEMS_vintage_2026-02-28.csv:0\nvintages/PAYEMS_vintage_2026-03-31.csv:0\nvintages/PAYEMS_vintage_2026-04-30.csv:0\nvintages/PAYEMS_vintage_2026-05-31.csv:0\nvintages/PAYEMS_vintage_2026-06-30.csv:0\nvintages/PAYEMS_vintage_2026-07-31.csv:0\nvintages/RSAFS_vintage_2025-10-31.csv:0\nvintages/RSAFS_vintage_2025-11-30.csv:0\nvintages/RSAFS_vintage_2025-12-31.csv:0\nvintages/RSAFS_vintage_2026-01-31.csv:0\nvintages/RSAFS_vintage_2026-02-28.csv:0\nvintages/RSAFS_vintage_2026-03-31.csv:0\nvintages/RSAFS_vintage_2026-04-30.csv:0\nvintages/RSAFS_vintage_2026-05-31.csv:0\nvintages/RSAFS_vintage_2026-06-30.csv:0\nvintages/RSAFS_vintage_2026-07-31.csv:0\nUMCSENT.csv:210\nRSAFS_current.csv:0","is_error":false}]},"uuid":"22e5b6d9-78d6-40b0-9bd7-cc771476f077","timestamp":"2026-10-30T05:33:42.001Z","toolUseResult":{"stdout":"# Data provenance — T1-forecast\n\nAll observed series were retrieved by the task designer on 2026-10-30 (between 04:00 and 04:01 UTC) from the Federal Reserve Bank of St. Louis FRED and ALFRED services, exactly as served (CSV, one header line, `observation_date` in YYYY-MM-DD, empty value field where the source reports no value). No value was altered. Row counts are data rows (lines after the header).\n\n| file | content | URL | retrieved (UTC) | data rows | SHA-256 |\n|---|---|---|---|---|---|\n| `vintages/PAYEMS_vintage_2025-10-31.csv` | PAYEMS as published on 2025-10-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-10-31` | 2026-10-30T04:00Z | 1040 | d4b4c909ed321fbf8021c385beb1512ac000009e22bb20bc27c791a248d33c60 |\n| `vintages/PAYEMS_vintage_2025-11-30.csv` | PAYEMS as published on 2025-11-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-11-30` | 2026-10-30T04:00Z | 1041 | a05ef7fc50cfc339207788f9d4205a619717bc44a335488d7790ae823bdb7b77 |\n| `vintages/PAYEMS_vintage_2025-12-31.csv` | PAYEMS as published on 2025-12-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-12-31` | 2026-10-30T04:00Z | 1043 | 829dae087af61916e87994220a3e5ebf5853fa43cc163b3798715603ec8bb031 |\n| `vintages/PAYEMS_vintage_2026-01-31.csv` | PAYEMS as published on 2026-01-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-01-31` | 2026-10-30T04:00Z | 1044 | 8fd01c5d2740e869458721a66b81bb2e247085e1e60cb93f3cb06bedf01885d7 |\n| `vintages/PAYEMS_vintage_2026-02-28.csv` | PAYEMS as published on 2026-02-28 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-02-28` | 2026-10-30T04:00Z | 1045 | 4a913e67ac5ffbb4d127f03f6f10ce0647998779629579617dabf1344d072db3 |\n| `vintages/PAYEMS_vintage_2026-03-31.csv` | PAYEMS as published on 2026-03-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-03-31` | 2026-10-30T04:00Z | 1046 | 919de86b07be90466e19844b54482ef8a3f6c8da7f9fe2a3ef3af0d02923c92e |\n| `vintages/PAYEMS_vintage_2026-04-30.csv` | PAYEMS as published on 2026-04-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-04-30` | 2026-10-30T04:00Z | 1047 | 06a8d663d9aaa61c6d5bda15d2893b10fd127b1d64fa16d47335341f62431c7a |\n| `vintages/PAYEMS_vintage_2026-05-31.csv` | PAYEMS as published on 2026-05-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-05-31` | 2026-10-30T04:00Z | 1048 | e2a152e874139acdebfc1715ffff9c1e5320f639f1443fff976b389724a5932d |\n| `vintages/PAYEMS_vintage_2026-06-30.csv` | PAYEMS as published on 2026-06-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-06-30` | 2026-10-30T04:00Z | 1049 | 19fbc6cd3547ffad1e22f32ac76f91758859804654b77622a4f42eac2d4e286a |\n| `vintages/PAYEMS_vintage_2026-07-31.csv` | PAYEMS as published on 2026-07-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-07-31` | 2026-10-30T04:00Z | 1050 | 3a3a6603fcc31c6eb2ee3293143888ab3440bbe3a962565eb39711b92019df41 |\n| `vintages/RSAFS_vintage_2025-10-31.csv` | RSAFS as published on 2025-10-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-10-31` | 2026-10-30T04:00Z | 404 | 3ecb0832a391e7261145af41a7a951f823df3a08d476162721022a0922d37e36 |\n| `vintages/RSAFS_vintage_2025-11-30.csv` | RSAFS as published on 2025-11-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-11-30` | 2026-10-30T04:00Z | 405 | 4a683d85374690f1e73d66c4f890156d0d32cd8b27d5d67b72582b9a0dd825a4 |\n| `vintages/RSAFS_vintage_2025-12-31.csv` | RSAFS as published on 2025-12-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-12-31` | 2026-10-30T04:00Z | 406 | 436df4b5509b1dfe61ea11b8f98dce27f012ff6b0255eb194eea564ad7e880dd |\n| `vintages/RSAFS_vintage_2026-01-31.csv` | RSAFS as published on 2026-01-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-01-31` | 2026-10-30T04:00Z | 407 | b92ab6a5c1dbdc5653e85c56fdb7dc0646f1fca7227ed6e64f1424e17446aa9e |\n| `vintages/RSAFS_vintage_2026-02-28.csv` | RSAFS as published on 2026-02-28 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-02-28` | 2026-10-30T04:00Z | 408 | 2af0a3aaeeba81efddb2613d326e3f96b5a1c88c41eb30ad384ab3cc4b289a77 |\n| `vintages/RSAFS_vintage_2026-03-31.csv` | RSAFS as published on 2026-03-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-03-31` | 2026-10-30T04:00Z | 409 | 0316fdf238d55d9f109b0be85e8c2d2eb00aee57016c0328d39405b349a8815f |\n| `vintages/RSAFS_vintage_2026-04-30.csv` | RSAFS as published on 2026-04-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-04-30` | 2026-10-30T04:00Z | 411 | 02dcc697a1db34753194974b1481dbd6a3ffcfe333864850e5ddd0e3202d2b8f |\n| `vintages/RSAFS_vintage_2026-05-31.csv` | RSAFS as published on 2026-05-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-05-31` | 2026-10-30T04:00Z | 412 | 8c3fe3530213589635ad6bb086f78ed613c459bf435c4558a5c51c597e19d87b |\n| `vintages/RSAFS_vintage_2026-06-30.csv` | RSAFS as published on 2026-06-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-06-30` | 2026-10-30T04:00Z | 413 | 2f103157f4a9b7a3c90c63af2033b9570d5e334205878714bbd35c8b304d7524 |\n| `vintages/RSAFS_vintage_2026-07-31.csv` | RSAFS as published on 2026-07-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-07-31` | 2026-10-30T04:00Z | 414 | 52c04a8fce2effa727f3ef267eaee04e13aef20f8aaabc982277d08fc5b8e5b2 |\n| `RSAFS_current.csv` | RSAFS (current vintage, all revisions to date) | `https://fred.stlouisfed.org/graph/fredgraph.csv?id=RSAFS` | 2026-10-30T04:00Z | 417 | 627d8bb386ac80f4aede3a9496b63040df584875fb766d455b08ebd84311468f |\n| `UMCSENT.csv` | UMCSENT (current vintage) | `https://fred.stlouisfed.org/graph/fredgraph.csv?id=UMCSENT` | 2026-10-30T04:00Z | 887 | 3076277f9f21f4bd3dc77fd68add84d417e5cce23f3adb96dc70313b3ad2dd46 |\n| `first_release.csv` | derived by the task designer from the month-end ALFRED vintages of RSAFS 2024-06-30 … 2026-10-29 (29 vintages; the 19 not shipped here were used only to date the first release of each month) | (derived) | 2026-10-30T04:00Z | 28 | 1d1ae8b3cacf7799aaefc7e0a969b3886e7979c36fce02ccf77d7c79fb7343e4 |\n\n## Series\n- **RSAFS** — Advance Retail Sales: Retail Trade and Food Services, Total. Source: U.S. Census Bureau, Advance Monthly Sales for Retail and Food Services (via FRED). Units: millions of dollars, seasonally adjusted, monthly, 1992-01 onward. The advance estimate of a month is normally published in the middle of the following month and is revised in the two following monthly releases and in annual revisions; a month-end ALFRED vintage therefore normally contains the months up to the month before the vintage date. The vintage grid in this bundle shows departures from this pattern (e.g. the vintages of 2025-10-31 and 2026-03-31 end two months before their date; the vintage of 2026-04-30 adds two months at once), which reflect the actual publication calendar; the task rules refer to the last month present in each vintage, not to the calendar.\n- **PAYEMS** — All Employees, Total Nonfarm. Source: U.S. Bureau of Labor Statistics, Current Employment Statistics (via FRED). Units: thousands of persons, seasonally adjusted, monthly, 1939-01 onward. Published in the Employment Situation release on the first Friday (normally) of the following month and revised in the two following releases and in annual benchmark revisions; the ALFRED vintages dated as the RSAFS vintages give the data available at each origin.\n- **UMCSENT** — University of Michigan: Consumer Sentiment, index 1966Q1 = 100, monthly (quarterly before 1978; empty fields where no survey was conducted). Source: University of Michigan, Surveys of Consumers (via FRED). The preliminary reading of a month is published in the middle of the month and the final reading at the end of the month; the series is not revised afterwards. The current FRED vintage is provided; at a month-end origin the final reading of the origin month is available (verified on the ALFRED vintages of 2025-10-31, 2026-03-31 and 2026-07-31, which contain the origin month).\n- **first_release.csv** — the first month-end ALFRED vintage in which each target month appears, the level printed there, the level of the month twelve months earlier in the same vintage, and the year-over-year growth 100·ln(ratio) (6 decimals). \"First release\" is defined at month-end granularity: for RSAFS the advance estimate is the only release of a month that can appear in the month-end vintage of the following month, so the value equals the advance estimate except where the publication calendar was disrupted (then it is the first published value as of the first month-end on which it existed).\n\n## Terms\nFRED/ALFRED data are provided by the Federal Reserve Bank of St. Louis under its terms of use (https://fred.stlouisfed.org/legal/); the underlying series are U.S. government statistics (Census Bureau, BLS: public domain) and the University of Michigan Surveys of Consumers index (redistributed by FRED with permission; cite \"University of Michigan, University of Michigan: Consumer Sentiment [UMCSENT], retrieved from FRED\"). The data are reproduced here unchanged for research use.\ntarget_month,first_release_vintage,level_first_release,level_prev_year_same_vintage,yoy_first_release\r\n2024-06-01,2024-07-31,704324,688630,2.253436\r\n2024-07-01,2024-08-31,709668,691250,2.62957\r\n2024-08-01,2024-09-30,710773,695940,2.108966\r\n2024-09-01,2024-10-31,714358,702160,1.722294\r\n2024-10-01,2024-11-30,718867,698956,2.808857\r\n2024-11-01,2024-12-31,724609,698105,3.726268\r\n2024-12-01,2025-01-31,729191,701672,3.846964\r\n2025-01-01,2025-02-28,723853,694696,4.111399\r\n2025-02-01,2025-03-31,722708,700932,3.059439\r\n2025-03-01,2025-04-30,722025,688248,4.791053\r\n2025-04-01,2025-05-31,724131,688629,5.026965\r\n2025-05-01,2025-06-30,715417,692635,3.236242\r\n2025-06-01,2025-07-31,720106,692922,3.848098\r\n2025-07-01,2025-08-31,726283,698909,3.84192\r\n2025-08-01,2025-09-30,732010,697157,4.878354\r\n2025-09-01,2025-11-30,733258,703285,4.17354\r\n2025-10-01,2025-12-31,732633,708070,3.410194\r\n2025-11-01,2026-01-31,735904,712175,3.277601\r\n2025-12-01,2026-02-28,734967,717547,2.398715\r\n2026-01-01,2026-03-31,733537,711095,3.1072\r\n2026-02-01,2026-04-30,739772,711575,3.886121\r\n2026-03-01,2026-04-30,752063,723350,3.89269\r\n2026-04-01,2026-05-31,757085,721903,4.758475\r\n2026-05-01,2026-06-30,763705,714568,6.650342\r\n2026-06-01,2026-07-31,768553,720164,6.503056\r\n2026-07-01,2026-08-31,763602,727176,4.887817\r\n2026-08-01,2026-09-30,737763,700218,5.223092\r\n2026-09-01,2026-10-29,745021,700948,6.09787\r\nvintages/PAYEMS_vintage_2025-10-31.csv 1041 observation_date,PAYEMS_20251031\n2025-06-01,159439\n2025-07-01,159518\n2025-08-01,159540\nvintages/PAYEMS_vintage_2025-11-30.csv 1042 observation_date,PAYEMS_20251130\n2025-07-01,159511\n2025-08-01,159507\n2025-09-01,159626\nvintages/PAYEMS_vintage_2025-12-31.csv 1044 observation_date,PAYEMS_20251231\n2025-09-01,159593\n2025-10-01,159488\n2025-11-01,159552\nvintages/PAYEMS_vintage_2026-01-31.csv 1045 observation_date,PAYEMS_20260131\n2025-10-01,159420\n2025-11-01,159476\n2025-12-01,159526\nvintages/PAYEMS_vintage_2026-02-28.csv 1046 observation_date,PAYEMS_20260228\n2025-11-01,158449\n2025-12-01,158497\n2026-01-01,158627\nvintages/PAYEMS_vintage_2026-03-31.csv 1047 observation_date,PAYEMS_20260331\n2025-12-01,158432\n2026-01-01,158558\n2026-02-01,158466\nvintages/PAYEMS_vintage_2026-04-30.csv 1048 observation_date,PAYEMS_20260430\n2026-01-01,158592\n2026-02-01,158459\n2026-03-01,158637\nvintages/PAYEMS_vintage_2026-05-31.csv 1049 observation_date,PAYEMS_20260531\n2026-02-01,158436\n2026-03-01,158621\n2026-04-01,158736\nvintages/PAYEMS_vintage_2026-06-30.csv 1050 observation_date,PAYEMS_20260630\n2026-03-01,158650\n2026-04-01,158829\n2026-05-01,159001\nvintages/PAYEMS_vintage_2026-07-31.csv 1051 observation_date,PAYEMS_20260731\n2026-04-01,158798\n2026-05-01,158927\n2026-06-01,158984\nvintages/RSAFS_vintage_2025-10-31.csv 405 observation_date,RSAFS_20251031\n2025-06-01,723033\n2025-07-01,727414\n2025-08-01,732010\nvintages/RSAFS_vintage_2025-11-30.csv 406 observation_date,RSAFS_20251130\n2025-07-01,727727\n2025-08-01,732059\n2025-09-01,733258\nvintages/RSAFS_vintage_2025-12-31.csv 407 observation_date,RSAFS_20251231\n2025-08-01,731700\n2025-09-01,732444\n2025-10-01,732633\nvintages/RSAFS_vintage_2026-01-31.csv 408 observation_date,RSAFS_20260131\n2025-09-01,732192\n2025-10-01,731412\n2025-11-01,735904\nvintages/RSAFS_vintage_2026-02-28.csv 409 observation_date,RSAFS_20260228\n2025-10-01,731051\n2025-11-01,735085\n2025-12-01,734967\nvintages/RSAFS_vintage_2026-03-31.csv 410 observation_date,RSAFS_20260331\n2025-11-01,734718\n2025-12-01,734685\n2026-01-01,733537\nvintages/RSAFS_vintage_2026-04-30.csv 412 observation_date,RSAFS_20260430\n2026-01-01,734503\n2026-02-01,739772\n2026-03-01,752063\nvintages/RSAFS_vintage_2026-05-31.csv 413 observation_date,RSAFS_20260531\n2026-02-01,741278\n2026-03-01,753370\n2026-04-01,757085\nvintages/RSAFS_vintage_2026-06-30.csv 414 observation_date,RSAFS_20260630\n2026-03-01,754013\n2026-04-01,757036\n2026-05-01,763705\nvintages/RSAFS_vintage_2026-07-31.csv 415 observation_date,RSAFS_20260731\n2026-04-01,759097\n2026-05-01,766876\n2026-06-01,768553\n2026-05-01,44.8\n2026-06-01,49.5\n2026-07-01,55.2\n2026-08-01,51.7\n2026-09-01,48.1\n2026-07-01,729915\n2026-08-01,739322\n2026-09-01,745021\nvintages/PAYEMS_vintage_2025-10-31.csv:0\nvintages/PAYEMS_vintage_2025-11-30.csv:0\nvintages/PAYEMS_vintage_2025-12-31.csv:0\nvintages/PAYEMS_vintage_2026-01-31.csv:0\nvintages/PAYEMS_vintage_2026-02-28.csv:0\nvintages/PAYEMS_vintage_2026-03-31.csv:0\nvintages/PAYEMS_vintage_2026-04-30.csv:0\nvintages/PAYEMS_vintage_2026-05-31.csv:0\nvintages/PAYEMS_vintage_2026-06-30.csv:0\nvintages/PAYEMS_vintage_2026-07-31.csv:0\nvintages/RSAFS_vintage_2025-10-31.csv:0\nvintages/RSAFS_vintage_2025-11-30.csv:0\nvintages/RSAFS_vintage_2025-12-31.csv:0\nvintages/RSAFS_vintage_2026-01-31.csv:0\nvintages/RSAFS_vintage_2026-02-28.csv:0\nvintages/RSAFS_vintage_2026-03-31.csv:0\nvintages/RSAFS_vintage_2026-04-30.csv:0\nvintages/RSAFS_vintage_2026-05-31.csv:0\nvintages/RSAFS_vintage_2026-06-30.csv:0\nvintages/RSAFS_vintage_2026-07-31.csv:0\nUMCSENT.csv:210\nRSAFS_current.csv:0","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"15a18081-530e-4555-be2a-27896b74de45","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0012/data","sessionId":"c66cce91-bbf6-4244-a056-ba0f0a9450c0","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"22e5b6d9-78d6-40b0-9bd7-cc771476f077","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsnRgbfKrnMWSPtuj9e","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015GT8wxKm1TJQWhVnGPgwe9","name":"Bash","input":{"command":"cd data; grep -n ',\\s*$\\|,\\.' vintages/* RSAFS_current.csv | head; awk -F, 'NR>1 && $2==\"\"' UMCSENT.csv | awk -F- '$1>=1978' | head"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4552,"cache_read_input_tokens":2234,"output_tokens":297,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":4552},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsnRPEVPBkrZbw9RfGy","requestRef":"1022abda-1f01-4ff2-9a68-dcfa05d4da98","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d896f385-c54c-4a32-a55f-9b01b09d4b8d","timestamp":"2026-10-30T05:33:42.713Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0012/data","sessionId":"c66cce91-bbf6-4244-a056-ba0f0a9450c0","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"d896f385-c54c-4a32-a55f-9b01b09d4b8d","isSidechain":false,"promptId":"ef574d05-d28e-41e2-b009-fce8ac301d89","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015GT8wxKm1TJQWhVnGPgwe9","type":"tool_result","content":"(eval):cd:1: no such file or directory: data","is_error":false}]},"uuid":"f0154678-56cd-495c-9886-91c142c0fdd0","timestamp":"2026-10-30T05:33:42.770Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d896f385-c54c-4a32-a55f-9b01b09d4b8d","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0012/data","sessionId":"c66cce91-bbf6-4244-a056-ba0f0a9450c0","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"c66cce91-bbf6-4244-a056-ba0f0a9450c0","hash":"0d09010f0b0cd1533f4375ab3bce6736b7463fd6ba8ab71f45111bd8c206696e","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_015CagmrwGTs6oW32wPTnCPj","name":"Bash","input":{"command":"cd data; cat README.md; cat first_release.csv; for f in vintages/*; do echo $f $(wc -l <$f) $(head -1 $f) ; tail -3 $f; done; tail -5 UMCSENT.csv; tail -3 RSAFS_current.csv; grep -c ',$' vintages/* UMCSENT.csv RSAFS_current.csv"}},{"type":"tool_use","id":"toolu_015GT8wxKm1TJQWhVnGPgwe9","name":"Bash","input":{"command":"cd data; grep -n ',\\s*$\\|,\\.' vintages/* RSAFS_current.csv | head; awk -F, 'NR>1 && $2==\"\"' UMCSENT.csv | awk -F- '$1>=1978' | head"}}]}}
{"type":"api-request-blob","sessionId":"c66cce91-bbf6-4244-a056-ba0f0a9450c0","hash":"49ebf335cc2494e439a92ee6fa6dd023890aedbae172e3f71e65765014c531dd","message":{"role":"user","content":[{"tool_use_id":"toolu_015CagmrwGTs6oW32wPTnCPj","type":"tool_result","content":"# Data provenance — T1-forecast\n\nAll observed series were retrieved by the task designer on 2026-10-30 (between 04:00 and 04:01 UTC) from the Federal Reserve Bank of St. Louis FRED and ALFRED services, exactly as served (CSV, one header line, `observation_date` in YYYY-MM-DD, empty value field where the source reports no value). No value was altered. Row counts are data rows (lines after the header).\n\n| file | content | URL | retrieved (UTC) | data rows | SHA-256 |\n|---|---|---|---|---|---|\n| `vintages/PAYEMS_vintage_2025-10-31.csv` | PAYEMS as published on 2025-10-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-10-31` | 2026-10-30T04:00Z | 1040 | d4b4c909ed321fbf8021c385beb1512ac000009e22bb20bc27c791a248d33c60 |\n| `vintages/PAYEMS_vintage_2025-11-30.csv` | PAYEMS as published on 2025-11-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-11-30` | 2026-10-30T04:00Z | 1041 | a05ef7fc50cfc339207788f9d4205a619717bc44a335488d7790ae823bdb7b77 |\n| `vintages/PAYEMS_vintage_2025-12-31.csv` | PAYEMS as published on 2025-12-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-12-31` | 2026-10-30T04:00Z | 1043 | 829dae087af61916e87994220a3e5ebf5853fa43cc163b3798715603ec8bb031 |\n| `vintages/PAYEMS_vintage_2026-01-31.csv` | PAYEMS as published on 2026-01-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-01-31` | 2026-10-30T04:00Z | 1044 | 8fd01c5d2740e869458721a66b81bb2e247085e1e60cb93f3cb06bedf01885d7 |\n| `vintages/PAYEMS_vintage_2026-02-28.csv` | PAYEMS as published on 2026-02-28 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-02-28` | 2026-10-30T04:00Z | 1045 | 4a913e67ac5ffbb4d127f03f6f10ce0647998779629579617dabf1344d072db3 |\n| `vintages/PAYEMS_vintage_2026-03-31.csv` | PAYEMS as published on 2026-03-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-03-31` | 2026-10-30T04:00Z | 1046 | 919de86b07be90466e19844b54482ef8a3f6c8da7f9fe2a3ef3af0d02923c92e |\n| `vintages/PAYEMS_vintage_2026-04-30.csv` | PAYEMS as published on 2026-04-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-04-30` | 2026-10-30T04:00Z | 1047 | 06a8d663d9aaa61c6d5bda15d2893b10fd127b1d64fa16d47335341f62431c7a |\n| `vintages/PAYEMS_vintage_2026-05-31.csv` | PAYEMS as published on 2026-05-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-05-31` | 2026-10-30T04:00Z | 1048 | e2a152e874139acdebfc1715ffff9c1e5320f639f1443fff976b389724a5932d |\n| `vintages/PAYEMS_vintage_2026-06-30.csv` | PAYEMS as published on 2026-06-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-06-30` | 2026-10-30T04:00Z | 1049 | 19fbc6cd3547ffad1e22f32ac76f91758859804654b77622a4f42eac2d4e286a |\n| `vintages/PAYEMS_vintage_2026-07-31.csv` | PAYEMS as published on 2026-07-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-07-31` | 2026-10-30T04:00Z | 1050 | 3a3a6603fcc31c6eb2ee3293143888ab3440bbe3a962565eb39711b92019df41 |\n| `vintages/RSAFS_vintage_2025-10-31.csv` | RSAFS as published on 2025-10-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-10-31` | 2026-10-30T04:00Z | 404 | 3ecb0832a391e7261145af41a7a951f823df3a08d476162721022a0922d37e36 |\n| `vintages/RSAFS_vintage_2025-11-30.csv` | RSAFS as published on 2025-11-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-11-30` | 2026-10-30T04:00Z | 405 | 4a683d85374690f1e73d66c4f890156d0d32cd8b27d5d67b72582b9a0dd825a4 |\n| `vintages/RSAFS_vintage_2025-12-31.csv` | RSAFS as published on 2025-12-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-12-31` | 2026-10-30T04:00Z | 406 | 436df4b5509b1dfe61ea11b8f98dce27f012ff6b0255eb194eea564ad7e880dd |\n| `vintages/RSAFS_vintage_2026-01-31.csv` | RSAFS as published on 2026-01-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-01-31` | 2026-10-30T04:00Z | 407 | b92ab6a5c1dbdc5653e85c56fdb7dc0646f1fca7227ed6e64f1424e17446aa9e |\n| `vintages/RSAFS_vintage_2026-02-28.csv` | RSAFS as published on 2026-02-28 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-02-28` | 2026-10-30T04:00Z | 408 | 2af0a3aaeeba81efddb2613d326e3f96b5a1c88c41eb30ad384ab3cc4b289a77 |\n| `vintages/RSAFS_vintage_2026-03-31.csv` | RSAFS as published on 2026-03-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-03-31` | 2026-10-30T04:00Z | 409 | 0316fdf238d55d9f109b0be85e8c2d2eb00aee57016c0328d39405b349a8815f |\n| `vintages/RSAFS_vintage_2026-04-30.csv` | RSAFS as published on 2026-04-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-04-30` | 2026-10-30T04:00Z | 411 | 02dcc697a1db34753194974b1481dbd6a3ffcfe333864850e5ddd0e3202d2b8f |\n| `vintages/RSAFS_vintage_2026-05-31.csv` | RSAFS as published on 2026-05-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-05-31` | 2026-10-30T04:00Z | 412 | 8c3fe3530213589635ad6bb086f78ed613c459bf435c4558a5c51c597e19d87b |\n| `vintages/RSAFS_vintage_2026-06-30.csv` | RSAFS as published on 2026-06-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-06-30` | 2026-10-30T04:00Z | 413 | 2f103157f4a9b7a3c90c63af2033b9570d5e334205878714bbd35c8b304d7524 |\n| `vintages/RSAFS_vintage_2026-07-31.csv` | RSAFS as published on 2026-07-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-07-31` | 2026-10-30T04:00Z | 414 | 52c04a8fce2effa727f3ef267eaee04e13aef20f8aaabc982277d08fc5b8e5b2 |\n| `RSAFS_current.csv` | RSAFS (current vintage, all revisions to date) | `https://fred.stlouisfed.org/graph/fredgraph.csv?id=RSAFS` | 2026-10-30T04:00Z | 417 | 627d8bb386ac80f4aede3a9496b63040df584875fb766d455b08ebd84311468f |\n| `UMCSENT.csv` | UMCSENT (current vintage) | `https://fred.stlouisfed.org/graph/fredgraph.csv?id=UMCSENT` | 2026-10-30T04:00Z | 887 | 3076277f9f21f4bd3dc77fd68add84d417e5cce23f3adb96dc70313b3ad2dd46 |\n| `first_release.csv` | derived by the task designer from the month-end ALFRED vintages of RSAFS 2024-06-30 … 2026-10-29 (29 vintages; the 19 not shipped here were used only to date the first release of each month) | (derived) | 2026-10-30T04:00Z | 28 | 1d1ae8b3cacf7799aaefc7e0a969b3886e7979c36fce02ccf77d7c79fb7343e4 |\n\n## Series\n- **RSAFS** — Advance Retail Sales: Retail Trade and Food Services, Total. Source: U.S. Census Bureau, Advance Monthly Sales for Retail and Food Services (via FRED). Units: millions of dollars, seasonally adjusted, monthly, 1992-01 onward. The advance estimate of a month is normally published in the middle of the following month and is revised in the two following monthly releases and in annual revisions; a month-end ALFRED vintage therefore normally contains the months up to the month before the vintage date. The vintage grid in this bundle shows departures from this pattern (e.g. the vintages of 2025-10-31 and 2026-03-31 end two months before their date; the vintage of 2026-04-30 adds two months at once), which reflect the actual publication calendar; the task rules refer to the last month present in each vintage, not to the calendar.\n- **PAYEMS** — All Employees, Total Nonfarm. Source: U.S. Bureau of Labor Statistics, Current Employment Statistics (via FRED). Units: thousands of persons, seasonally adjusted, monthly, 1939-01 onward. Published in the Employment Situation release on the first Friday (normally) of the following month and revised in the two following releases and in annual benchmark revisions; the ALFRED vintages dated as the RSAFS vintages give the data available at each origin.\n- **UMCSENT** — University of Michigan: Consumer Sentiment, index 1966Q1 = 100, monthly (quarterly before 1978; empty fields where no survey was conducted). Source: University of Michigan, Surveys of Consumers (via FRED). The preliminary reading of a month is published in the middle of the month and the final reading at the end of the month; the series is not revised afterwards. The current FRED vintage is provided; at a month-end origin the final reading of the origin month is available (verified on the ALFRED vintages of 2025-10-31, 2026-03-31 and 2026-07-31, which contain the origin month).\n- **first_release.csv** — the first month-end ALFRED vintage in which each target month appears, the level printed there, the level of the month twelve months earlier in the same vintage, and the year-over-year growth 100·ln(ratio) (6 decimals). \"First release\" is defined at month-end granularity: for RSAFS the advance estimate is the only release of a month that can appear in the month-end vintage of the following month, so the value equals the advance estimate except where the publication calendar was disrupted (then it is the first published value as of the first month-end on which it existed).\n\n## Terms\nFRED/ALFRED data are provided by the Federal Reserve Bank of St. Louis under its terms of use (https://fred.stlouisfed.org/legal/); the underlying series are U.S. government statistics (Census Bureau, BLS: public domain) and the University of Michigan Surveys of Consumers index (redistributed by FRED with permission; cite \"University of Michigan, University of Michigan: Consumer Sentiment [UMCSENT], retrieved from FRED\"). The data are reproduced here unchanged for research use.\ntarget_month,first_release_vintage,level_first_release,level_prev_year_same_vintage,yoy_first_release\r\n2024-06-01,2024-07-31,704324,688630,2.253436\r\n2024-07-01,2024-08-31,709668,691250,2.62957\r\n2024-08-01,2024-09-30,710773,695940,2.108966\r\n2024-09-01,2024-10-31,714358,702160,1.722294\r\n2024-10-01,2024-11-30,718867,698956,2.808857\r\n2024-11-01,2024-12-31,724609,698105,3.726268\r\n2024-12-01,2025-01-31,729191,701672,3.846964\r\n2025-01-01,2025-02-28,723853,694696,4.111399\r\n2025-02-01,2025-03-31,722708,700932,3.059439\r\n2025-03-01,2025-04-30,722025,688248,4.791053\r\n2025-04-01,2025-05-31,724131,688629,5.026965\r\n2025-05-01,2025-06-30,715417,692635,3.236242\r\n2025-06-01,2025-07-31,720106,692922,3.848098\r\n2025-07-01,2025-08-31,726283,698909,3.84192\r\n2025-08-01,2025-09-30,732010,697157,4.878354\r\n2025-09-01,2025-11-30,733258,703285,4.17354\r\n2025-10-01,2025-12-31,732633,708070,3.410194\r\n2025-11-01,2026-01-31,735904,712175,3.277601\r\n2025-12-01,2026-02-28,734967,717547,2.398715\r\n2026-01-01,2026-03-31,733537,711095,3.1072\r\n2026-02-01,2026-04-30,739772,711575,3.886121\r\n2026-03-01,2026-04-30,752063,723350,3.89269\r\n2026-04-01,2026-05-31,757085,721903,4.758475\r\n2026-05-01,2026-06-30,763705,714568,6.650342\r\n2026-06-01,2026-07-31,768553,720164,6.503056\r\n2026-07-01,2026-08-31,763602,727176,4.887817\r\n2026-08-01,2026-09-30,737763,700218,5.223092\r\n2026-09-01,2026-10-29,745021,700948,6.09787\r\nvintages/PAYEMS_vintage_2025-10-31.csv 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{"parentUuid":"f87872c4-488a-4ba8-8425-fa7c6349fd89","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsogAWmKifzcPyhvHxs","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1489,"cache_read_input_tokens":15980,"output_tokens":6042,"output_tokens_details":{"thinking_tokens":1257},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1489},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":12581,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsofseh6SQQnvBp35RR","requestRef":"b9cc2324-7284-4b3a-98a7-c5bb67cb678e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"01591692-fea2-4c3e-b75b-aa10de2e92bd","timestamp":"2026-10-30T05:34:10.455Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0012/data","sessionId":"c66cce91-bbf6-4244-a056-ba0f0a9450c0","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"01591692-fea2-4c3e-b75b-aa10de2e92bd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsogAWmKifzcPyhvHxs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LHsZNJ1fVo8xs1p9fzhPdN","name":"Bash","input":{"command":"mkdir -p /Users/simon-pierreboucher/wp29/runs/RT0012/out; cat > /Users/simon-pierreboucher/wp29/runs/RT0012/out/analysis.py <<'EOF'\n\"\"\"T1-forecast: real-time forecasting of US retail-sales YoY growth from ALFRED vintages.\nRun from workspace root:  ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nDATA, OUT = \"data\", \"out\"\nORIGINS = [\"2025-10-31\", \"2025-11-30\", \"2025-12-31\", \"2026-01-31\", \"2026-02-28\",\n           \"2026-03-31\", \"2026-04-30\", \"2026-05-31\", \"2026-06-30\", \"2026-07-31\"]\nHORIZONS = [1, 3]\nDRIFT_WINDOW = 36  # months of m/m log changes used for the drift (fixed a priori)\nSEED = 20261030\nn_rows = {}\n\ndef read(path):\n    d = pd.read_csv(os.path.join(DATA, path), parse_dates=[\"observation_date\"])\n    n_rows[path] = int(len(d))\n    s = d.set_index(\"observation_date\").iloc[:, 0].astype(float)\n    s.index.freq = None\n    return s\n\ndef yoy(level):\n    return 100 * np.log(level / level.shift(12))\n\nfr = pd.read_csv(os.path.join(DATA, \"first_release.csv\"), parse_dates=[\"target_month\"])\nn_rows[\"first_release.csv\"] = int(len(fr))\nactual = fr.set_index(\"target_month\")[\"yoy_first_release\"]\n\ndef add_months(ts, k):\n    return ts + pd.DateOffset(months=k)\n\nrows, audit = [], []\nfor V in ORIGINS:\n    L = read(f\"vintages/RSAFS_vintage_{V}.csv\")\n    P = read(f\"vintages/PAYEMS_vintage_{V}.csv\")  # read for audit; not used by the model\n    assert L.isna().sum() == 0\n    g = yoy(L)\n    last = L.index[-1]\n    vdate = pd.Timestamp(V)\n    lag = (vdate.year - last.year) * 12 + vdate.month - last.month\n    audit.append({\"origin\": V, \"last_month\": str(last.date()), \"months_behind\": int(lag),\n                  \"payems_last\": str(P.index[-1].date())})\n    # AR(1) on g, pairs with 1993-02 <= m <= last\n    gg = g.dropna()\n    y = gg.loc[\"1993-02-01\":last]\n    x = gg.shift(1).loc[y.index]\n    X = np.column_stack([np.ones(len(y)), x.values])\n    beta = np.linalg.lstsq(X, y.values, rcond=None)[0]\n    # model: drift-in-levels with exact base effects\n    dl = 100 * np.log(L).diff().dropna()\n    mu = dl.iloc[-DRIFT_WINDOW:].mean()\n    for h in HORIZONS:\n        T = add_months(last, h)\n        f_naive = g.loc[last]\n        f_snaive = g.loc[add_months(T, -12)]\n        f = g.loc[last]\n        for _ in range(h):\n            f = beta[0] + beta[1] * f\n        f_ar1 = f\n        # g_T = 100 ln(L_last/L_{T-12}) + h*mu  (L_{T-12} is in the vintage)\n        f_model = 100 * np.log(L.loc[last] / L.loc[add_months(T, -12)]) + h * mu\n        for name, val in [(\"naive\", f_naive), (\"snaive\", f_snaive), (\"ar1\", f_ar1), (\"model\", f_model)]:\n            rows.append({\"origin_date\": V, \"target_date\": T.strftime(\"%Y-%m-%d\"), \"horizon\": h,\n                         \"forecast\": float(val), \"actual_first_release\": float(actual.loc[T]),\n                         \"model\": name, \"vintage_used\": V})\n\nfc = pd.DataFrame(rows)\nfc[\"error\"] = fc[\"actual_first_release\"] - fc[\"forecast\"]\nfc.drop(columns=\"error\").to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6f\")\n\ntab = []\nfor m in [\"naive\", \"snaive\", \"ar1\", \"model\"]:\n    for h in HORIZONS:\n        e = fc[(fc.model == m) & (fc.horizon == h)][\"error\"].values\n        tab.append({\"model\": m, \"horizon\": h, \"rmse\": float(np.sqrt(np.mean(e**2))),\n                    \"mae\": float(np.mean(np.abs(e))), \"n\": int(len(e))})\ntab = pd.DataFrame(tab)\ntab.to_csv(os.path.join(OUT, \"table1.csv\"), index=False, float_format=\"%.6f\")\n\ndef dm(e_naive, e_model, h):\n    d = e_naive**2 - e_model**2\n    n = len(d); dbar = d.mean(); dc = d - dbar\n    lrv = np.sum(dc**2) / n\n    for j in range(1, h):\n        w = 1 - j / h\n        lrv += 2 * w * np.sum(dc[j:] * dc[:-j]) / n\n    stat = dbar / np.sqrt(lrv / n)\n    return float(stat), float(2 * (1 - stats.norm.cdf(abs(stat))))\n\nres = {\"task_id\": \"T1-forecast\",\n       \"model_name\": \"Drift-in-levels with exact base effects (RW-drift36)\",\n       \"model_description\": (\n           \"The YoY growth of target month T = last(V)+h is written exactly as \"\n           \"g_T = 100*ln(L_last/L_{T-12}) + sum_{k=1..h} 100*dln L_{last+k}. The first term uses only \"\n           \"levels published in the RSAFS vintage dated V (L_{T-12} is always in the vintage), so the \"\n           \"'base effect' of months dropping out of the 12-month window is known exactly at the origin. \"\n           \"The unknown future monthly log changes are each forecast by their mean over the last 36 \"\n           \"monthly log changes available in the same vintage (a random walk with drift in log levels; \"\n           \"window fixed a priori, not tuned on evaluation targets). Re-estimated at every origin from the \"\n           \"vintage dated V only; PAYEMS and UMCSENT are not used (no information leakage possible); \"\n           \"RSAFS_current is not used.\"),\n       \"n_origins\": len(ORIGINS), \"first_origin\": ORIGINS[0], \"last_origin\": ORIGINS[-1]}\nfor _, r in tab.iterrows():\n    res[f\"rmse_{r.model}_h{r.horizon}\"] = r.rmse\n    res[f\"mae_{r.model}_h{r.horizon}\"] = r.mae\n\nrng = np.random.default_rng(SEED)\nboot = {}\nfor h in HORIZONS:\n    en = fc[(fc.model == \"naive\") & (fc.horizon == h)].sort_values(\"origin_date\")[\"error\"].values\n    em = fc[(fc.model == \"model\") & (fc.horizon == h)].sort_values(\"origin_date\")[\"error\"].values\n    res[f\"rel_improvement_rmse_h{h}\"] = float(1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"])\n    s, p = dm(en, em, h)\n    res[f\"dm_stat_h{h}\"], res[f\"dm_pvalue_h{h}\"] = s, p\n    better = res[f\"rmse_model_h{h}\"] < res[f\"rmse_naive_h{h}\"]\n    sig = p < 0.05\n    res[f\"conclusion_code_h{h}\"] = (\"model_better_\" if better else \"naive_better_\") + (\"significant\" if sig else \"not_significant\")\n    # circular moving-block bootstrap (block length max(h,2)) of RMSE_naive - RMSE_model\n    n = len(en); b = max(h, 2); B = 9999; diffs = np.empty(B)\n    for i in range(B):\n        starts = rng.integers(0, n, int(np.ceil(n / b)))\n        idx = np.concatenate([(np.arange(s0, s0 + b) % n) for s0 in starts])[:n]\n        diffs[i] = np.sqrt(np.mean(en[idx]**2)) - np.sqrt(np.mean(em[idx]**2))\n    boot[h] = {\"rmse_diff_naive_minus_model\": float(np.sqrt(np.mean(en**2)) - np.sqrt(np.mean(em**2))),\n               \"ci95\": [float(np.percentile(diffs, 2.5)), float(np.percentile(diffs, 97.5))],\n               \"share_positive\": float(np.mean(diffs > 0))}\n\n# (d) revisions\ncur = read(\"RSAFS_current.csv\")\ngcur = yoy(cur)\nt1 = fc[(fc.model == \"naive\") & (fc.horizon == 1)][\"target_date\"].map(pd.Timestamp)\nrevs = [(str(T.date()), float(gcur.loc[T]), float(actual.loc[T]), float(gcur.loc[T] - actual.loc[T])) for T in t1]\nres[\"mean_abs_revision_h1_targets\"] = float(np.mean([abs(r[3]) for r in revs]))\n\num = read(\"UMCSENT.csv\")  # read for audit only; not used by the model\nres[\"feasibility\"] = \"complete\"\nres[\"feasibility_reason\"] = None\nlag_txt = \"; \".join(f\"{a['origin']}->last {a['last_month'][:7]} ({a['months_behind']}m)\" for a in audit)\nres[\"data_issues\"] = [\n    {\"issue\": \"Irregular RSAFS publication timing: last(V) is not always the month before V. \" + lag_txt,\n     \"handling\": \"Targets defined relative to last(V) as required (T = last(V)+h); origins 2025-10-31 and 2026-03-31 forecast from a vintage two months behind, so their h=1 target is the month that is normally already published.\"},\n    {\"issue\": \"Two target months (2026-02, 2026-03) were first released in the same month-end vintage (2026-04-30); 2025-09 first appeared only in the 2025-11-30 vintage (publication disruption).\",\n     \"handling\": \"Actuals taken as given from first_release.csv (first month-end appearance); no adjustment.\"},\n    {\"issue\": \"Annual/benchmark revision of RSAFS between the 2026-08-31 and 2026-09-30 vintages lowered the level of the whole series by roughly 4% (e.g. 2025-08: 731700 in the 2026-07-31 vintage vs 700407 in the current file); first_release levels for 2026-08 onward are on the new basis.\",\n     \"handling\": \"All comparisons use log YoY growth computed within a single vintage, which is largely invariant to proportional level rebasing; documented as affecting estimand (d).\"},\n    {\"issue\": \"PAYEMS vintage 2026-02-28 contains the annual benchmark revision (level about 1 million lower than in the 2026-01-31 vintage).\",\n     \"handling\": \"PAYEMS not used by the model; noted only.\"},\n    {\"issue\": \"UMCSENT has 210 empty values, all before 1978 (quarterly/irregular survey period).\",\n     \"handling\": \"UMCSENT not used by the model; no imputation needed.\"},\n    {\"issue\": \"RSAFS 2026-07 first release (763602, old basis) vs current value 729915 (new basis): level comparisons across vintages are not meaningful.\",\n     \"handling\": \"Revision measure (d) computed on growth rates only.\"},\n]\nres[\"n_input_rows\"] = n_rows\nres[\"n_analysed\"] = int(len(fc[fc.model == \"model\"]))\nres[\"random_seed\"] = SEED\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\nwith open(os.path.join(OUT, \"supplement.json\"), \"w\") as f:\n    json.dump({\"publication_audit\": audit, \"bootstrap\": boot, \"revisions_h1\": revs,\n               \"ar1_note\": \"see forecasts.csv\"}, f, indent=2)\n\nfig, axes = plt.subplots(2, 1, figsize=(8, 7), sharex=True)\nfor ax, h in zip(axes, HORIZONS):\n    sub = fc[fc.horizon == h]\n    a = sub[sub.model == \"naive\"].sort_values(\"target_date\")\n    t = pd.to_datetime(a.target_date)\n    ax.plot(t, a.actual_first_release, \"k-o\", label=\"first-release actual\")\n    for m, st in [(\"naive\", \"C0--s\"), (\"ar1\", \"C2:^\"), (\"model\", \"C3-d\")]:\n        s2 = sub[sub.model == m].sort_values(\"target_date\")\n        ax.plot(pd.to_datetime(s2.target_date), s2.forecast, st, label=m)\n    ax.set_title(f\"h = {h}\"); ax.set_ylabel(\"YoY growth, %\"); ax.grid(alpha=.3)\naxes[0].legend(fontsize=8)\nfig.suptitle(\"US retail & food services sales YoY growth: real-time forecasts vs first release\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=130)\nprint(json.dumps({k: v for k, v in res.items() if k not in (\"data_issues\", \"model_description\")}, indent=1))\nEOF\ncd /Users/simon-pierreboucher/wp29/runs/RT0012 && ~/wp29/venv/bin/python out/analysis.py && cat out/supplement.json && cat out/forecasts.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1489,"cache_read_input_tokens":15980,"output_tokens":6042,"output_tokens_details":{"thinking_tokens":1257},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1489},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsofseh6SQQnvBp35RR","requestRef":"b9cc2324-7284-4b3a-98a7-c5bb67cb678e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5d049819-65a0-43bf-9ed7-164974c9cf49","timestamp":"2026-10-30T05:34:47.241Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0012/data","sessionId":"c66cce91-bbf6-4244-a056-ba0f0a9450c0","version":"2.1.314","gitBranch":"HEAD"}
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0.4970233368707781,\n \"rmse_model_h3\": 1.0538489260016293,\n \"mae_model_h3\": 0.7471097183497802,\n \"rel_improvement_rmse_h1\": 0.4458427959311808,\n \"dm_stat_h1\": 2.302120035797033,\n \"dm_pvalue_h1\": 0.021328403819426134,\n \"conclusion_code_h1\": \"model_better_significant\",\n \"rel_improvement_rmse_h3\": 0.25787396930332507,\n \"dm_stat_h3\": 1.8644851017183233,\n \"dm_pvalue_h3\": 0.06225361183400402,\n \"conclusion_code_h3\": \"model_better_not_significant\",\n \"mean_abs_revision_h1_targets\": 0.5482198028388884,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"n_input_rows\": {\n  \"first_release.csv\": 28,\n  \"vintages/RSAFS_vintage_2025-10-31.csv\": 404,\n  \"vintages/PAYEMS_vintage_2025-10-31.csv\": 1040,\n  \"vintages/RSAFS_vintage_2025-11-30.csv\": 405,\n  \"vintages/PAYEMS_vintage_2025-11-30.csv\": 1041,\n  \"vintages/RSAFS_vintage_2025-12-31.csv\": 406,\n  \"vintages/PAYEMS_vintage_2025-12-31.csv\": 1043,\n  \"vintages/RSAFS_vintage_2026-01-31.csv\": 407,\n  \"vintages/PAYEMS_vintage_2026-01-31.csv\": 1044,\n  \"vintages/RSAFS_vintage_2026-02-28.csv\": 408,\n  \"vintages/PAYEMS_vintage_2026-02-28.csv\": 1045,\n  \"vintages/RSAFS_vintage_2026-03-31.csv\": 409,\n  \"vintages/PAYEMS_vintage_2026-03-31.csv\": 1046,\n  \"vintages/RSAFS_vintage_2026-04-30.csv\": 411,\n  \"vintages/PAYEMS_vintage_2026-04-30.csv\": 1047,\n  \"vintages/RSAFS_vintage_2026-05-31.csv\": 412,\n  \"vintages/PAYEMS_vintage_2026-05-31.csv\": 1048,\n  \"vintages/RSAFS_vintage_2026-06-30.csv\": 413,\n  \"vintages/PAYEMS_vintage_2026-06-30.csv\": 1049,\n  \"vintages/RSAFS_vintage_2026-07-31.csv\": 414,\n  \"vintages/PAYEMS_vintage_2026-07-31.csv\": 1050,\n  \"RSAFS_current.csv\": 417,\n  \"UMCSENT.csv\": 887\n },\n \"n_analysed\": 20,\n \"random_seed\": 20261030\n}\n{\n  \"publication_audit\": [\n    {\n      \"origin\": \"2025-10-31\",\n      \"last_month\": \"2025-08-01\",\n      \"months_behind\": 2,\n      \"payems_last\": \"2025-08-01\"\n    },\n    {\n      \"origin\": \"2025-11-30\",\n      \"last_month\": \"2025-09-01\",\n      \"months_behind\": 2,\n      \"payems_last\": \"2025-09-01\"\n    },\n    {\n      \"origin\": \"2025-12-31\",\n      \"last_month\": \"2025-10-01\",\n      \"months_behind\": 2,\n      \"payems_last\": \"2025-11-01\"\n    },\n    {\n      \"origin\": \"2026-01-31\",\n      \"last_month\": \"2025-11-01\",\n      \"months_behind\": 2,\n      \"payems_last\": \"2025-12-01\"\n    },\n    {\n      \"origin\": \"2026-02-28\",\n      \"last_month\": \"2025-12-01\",\n      \"months_behind\": 2,\n      \"payems_last\": \"2026-01-01\"\n    },\n    {\n      \"origin\": \"2026-03-31\",\n      \"last_month\": \"2026-01-01\",\n      \"months_behind\": 2,\n      \"payems_last\": \"2026-02-01\"\n    },\n    {\n      \"origin\": \"2026-04-30\",\n      \"last_month\": \"2026-03-01\",\n      \"months_behind\": 1,\n      \"payems_last\": \"2026-03-01\"\n    },\n    {\n      \"origin\": \"2026-05-31\",\n      \"last_month\": \"2026-04-01\",\n      \"months_behind\": 1,\n      \"payems_last\": \"2026-04-01\"\n    },\n    {\n      \"origin\": \"2026-06-30\",\n      \"last_month\": \"2026-05-01\",\n      \"months_behind\": 1,\n      \"payems_last\": \"2026-05-01\"\n    },\n    {\n      \"origin\": \"2026-07-31\",\n      \"last_month\": \"2026-06-01\",\n      \"months_behind\": 1,\n      \"payems_last\": \"2026-06-01\"\n    }\n  ],\n  \"bootstrap\": {\n    \"1\": {\n      \"rmse_diff_naive_minus_model\": 0.44444924822569176,\n      \"ci95\": [\n        0.2605365693579822,\n        0.6456694555820662\n      ],\n      \"share_positive\": 1.0\n    },\n    \"3\": {\n      \"rmse_diff_naive_minus_model\": 0.36619144775041756,\n      \"ci95\": [\n        -0.029850618374571158,\n        0.9056331601066206\n      ],\n      \"share_positive\": 0.9604960496049605\n    }\n  },\n  \"revisions_h1\": [\n    [\n      \"2025-09-01\",\n      3.62332320745983,\n      4.17354,\n      -0.5502167925401702\n    ],\n    [\n      \"2025-10-01\",\n      2.7460176832785423,\n      3.410194,\n      -0.6641763167214578\n    ],\n    [\n      \"2025-11-01\",\n      2.83868101772185,\n      3.277601,\n      -0.4389199822781502\n    ],\n    [\n      \"2025-12-01\",\n      2.0700881834455918,\n      2.398715,\n      -0.3286268165544084\n    ],\n    [\n      \"2026-01-01\",\n      2.1315240242387765,\n      3.1072,\n      -0.9756759757612237\n    ],\n    [\n      \"2026-02-01\",\n      3.4761653691061247,\n      3.886121,\n      -0.40995563089387543\n    ],\n    [\n      \"2026-04-01\",\n      4.010302170898596,\n      4.758475,\n      -0.7481728291014038\n    ],\n    [\n      \"2026-05-01\",\n      6.485002073991283,\n      6.650342,\n      -0.16533992600871716\n    ],\n    [\n      \"2026-06-01\",\n      5.7589671864483325,\n      6.503056,\n      -0.7440888135516674\n    ],\n    [\n      \"2026-07-01\",\n      4.430792055022191,\n      4.887817,\n      -0.45702494497780943\n    ]\n  ],\n  \"ar1_note\": \"see forecasts.csv\"\n}origin_date,target_date,horizon,forecast,actual_first_release,model,vintage_used\n2025-10-31,2025-09-01,1,4.878354,4.173540,naive,2025-10-31\n2025-10-31,2025-09-01,1,1.964326,4.173540,snaive,2025-10-31\n2025-10-31,2025-09-01,1,4.821776,4.173540,ar1,2025-10-31\n2025-10-31,2025-09-01,1,4.305174,4.173540,model,2025-10-31\n2025-10-31,2025-11-01,3,4.878354,3.277601,naive,2025-10-31\n2025-10-31,2025-11-01,3,3.838399,3.277601,snaive,2025-10-31\n2025-10-31,2025-11-01,3,4.734345,3.277601,ar1,2025-10-31\n2025-10-31,2025-11-01,3,3.569312,3.277601,model,2025-10-31\n2025-11-30,2025-10-01,1,4.173540,3.410194,naive,2025-11-30\n2025-11-30,2025-10-01,1,3.080399,3.410194,snaive,2025-11-30\n2025-11-30,2025-10-01,1,4.228201,3.410194,ar1,2025-11-30\n2025-11-30,2025-10-01,1,3.844619,3.410194,model,2025-11-30\n2025-11-30,2025-12-01,3,4.173540,2.398715,naive,2025-11-30\n2025-11-30,2025-12-01,3,4.471737,2.398715,snaive,2025-11-30\n2025-11-30,2025-12-01,3,4.312665,2.398715,ar1,2025-11-30\n2025-11-30,2025-12-01,3,3.003663,2.398715,model,2025-11-30\n2025-12-31,2025-11-01,1,3.410194,3.277601,naive,2025-12-31\n2025-12-31,2025-11-01,1,3.838399,3.277601,snaive,2025-12-31\n2025-12-31,2025-11-01,1,3.584932,3.277601,ar1,2025-12-31\n2025-12-31,2025-11-01,1,3.090093,3.277601,model,2025-12-31\n2025-12-31,2026-01-01,3,3.410194,3.107200,naive,2025-12-31\n2025-12-31,2026-01-01,3,4.455744,3.107200,snaive,2025-12-31\n2025-12-31,2026-01-01,3,3.854961,3.107200,ar1,2025-12-31\n2025-12-31,2026-01-01,3,3.693702,3.107200,model,2025-12-31\n2026-01-31,2025-12-01,1,3.277601,2.398715,naive,2026-01-31\n2026-01-31,2025-12-01,1,4.471737,2.398715,snaive,2026-01-31\n2026-01-31,2025-12-01,1,3.472669,2.398715,ar1,2026-01-31\n2026-01-31,2025-12-01,1,2.814767,2.398715,model,2026-01-31\n2026-01-31,2026-02-01,3,3.277601,3.886121,naive,2026-01-31\n2026-01-31,2026-02-01,3,3.790905,3.886121,snaive,2026-01-31\n2026-01-31,2026-02-01,3,3.774129,3.886121,ar1,2026-01-31\n2026-01-31,2026-02-01,3,4.250323,3.886121,model,2026-01-31\n2026-02-28,2026-01-01,1,2.398715,3.107200,naive,2026-02-28\n2026-02-28,2026-01-01,1,4.455744,3.107200,snaive,2026-02-28\n2026-02-28,2026-01-01,1,2.731303,3.107200,ar1,2026-02-28\n2026-02-28,2026-01-01,1,3.584235,3.107200,model,2026-02-28\n2026-02-28,2026-03-01,3,2.398715,3.892690,naive,2026-02-28\n2026-02-28,2026-03-01,3,4.955017,3.892690,snaive,2026-02-28\n2026-02-28,2026-03-01,3,3.245443,3.892690,ar1,2026-02-28\n2026-02-28,2026-03-01,3,2.702058,3.892690,model,2026-02-28\n2026-03-31,2026-02-01,1,3.107200,3.886121,naive,2026-03-31\n2026-03-31,2026-02-01,1,3.790905,3.886121,snaive,2026-03-31\n2026-03-31,2026-02-01,1,3.327514,3.886121,ar1,2026-03-31\n2026-03-31,2026-02-01,1,3.223629,3.886121,model,2026-03-31\n2026-03-31,2026-04-01,3,3.107200,4.758475,naive,2026-03-31\n2026-03-31,2026-04-01,3,4.852267,4.758475,snaive,2026-03-31\n2026-03-31,2026-04-01,3,3.668045,4.758475,ar1,2026-03-31\n2026-03-31,2026-04-01,3,2.242962,4.758475,model,2026-03-31\n2026-04-30,2026-04-01,1,3.892690,4.758475,naive,2026-04-30\n2026-04-30,2026-04-01,1,4.852267,4.758475,snaive,2026-04-30\n2026-04-30,2026-04-01,1,3.988685,4.758475,ar1,2026-04-30\n2026-04-30,2026-04-01,1,4.450186,4.758475,model,2026-04-30\n2026-04-30,2026-06-01,3,3.892690,6.503056,naive,2026-04-30\n2026-04-30,2026-06-01,3,4.322028,6.503056,snaive,2026-04-30\n2026-04-30,2026-06-01,3,4.137030,6.503056,ar1,2026-04-30\n2026-04-30,2026-06-01,3,4.960909,6.503056,model,2026-04-30\n2026-05-31,2026-05-01,1,4.758475,6.650342,naive,2026-05-31\n2026-05-31,2026-05-01,1,3.311739,6.650342,snaive,2026-05-31\n2026-05-31,2026-05-01,1,4.717932,6.650342,ar1,2026-05-31\n2026-05-31,2026-05-01,1,5.902822,6.650342,model,2026-05-31\n2026-05-31,2026-07-01,3,4.758475,4.887817,naive,2026-05-31\n2026-05-31,2026-07-01,3,4.051131,4.887817,snaive,2026-05-31\n2026-05-31,2026-07-01,3,4.655286,4.887817,ar1,2026-05-31\n2026-05-31,2026-07-01,3,4.967129,4.887817,model,2026-05-31\n2026-06-30,2026-06-01,1,6.650342,6.503056,naive,2026-06-30\n2026-06-30,2026-06-01,1,4.322028,6.503056,snaive,2026-06-30\n2026-06-30,2026-06-01,1,6.311495,6.503056,ar1,2026-06-30\n2026-06-30,2026-06-01,1,5.820164,6.503056,model,2026-06-30\n2026-06-30,2026-08-01,3,6.650342,5.223092,naive,2026-06-30\n2026-06-30,2026-08-01,3,4.852923,5.223092,snaive,2026-06-30\n2026-06-30,2026-08-01,3,5.787951,5.223092,ar1,2026-06-30\n2026-06-30,2026-08-01,3,5.323574,5.223092,model,2026-06-30\n2026-07-31,2026-07-01,1,6.503056,4.887817,naive,2026-07-31\n2026-07-31,2026-07-01,1,4.051131,4.887817,snaive,2026-07-31\n2026-07-31,2026-07-01,1,6.188534,4.887817,ar1,2026-07-31\n2026-07-31,2026-07-01,1,5.810203,4.887817,model,2026-07-31\n2026-07-31,2026-09-01,3,6.503056,6.097870,naive,2026-07-31\n2026-07-31,2026-09-01,3,4.060765,6.097870,snaive,2026-07-31\n2026-07-31,2026-09-01,3,5.702516,6.097870,ar1,2026-07-31\n2026-07-31,2026-09-01,3,5.902221,6.097870,model,2026-07-31","is_error":false}]},"uuid":"ed9d048f-a57e-4a94-a3fe-94d08a350752","timestamp":"2026-10-30T05:34:48.153Z","toolUseResult":{"stdout":"{\n \"task_id\": \"T1-forecast\",\n \"model_name\": \"Drift-in-levels with exact base effects (RW-drift36)\",\n \"n_origins\": 10,\n \"first_origin\": \"2025-10-31\",\n \"last_origin\": \"2026-07-31\",\n \"rmse_naive_h1\": 0.9968743518607753,\n \"mae_naive_h1\": 0.8487221880004494,\n \"rmse_naive_h3\": 1.4200403737520468,\n \"mae_naive_h3\": 1.2004486337876656,\n \"rmse_snaive_h1\": 1.6745428991342528,\n \"mae_snaive_h1\": 1.3066698762542999,\n \"rmse_snaive_h3\": 1.3160788989552046,\n \"mae_snaive_h3\": 1.065868691479163,\n \"rmse_ar1_h1\": 0.9405244111185092,\n \"mae_ar1_h1\": 0.7976510573253133,\n \"rmse_ar1_h3\": 1.1871178147423016,\n \"mae_ar1_h3\": 0.9526895961443078,\n \"rmse_model_h1\": 0.5524251036350836,\n \"mae_model_h1\": 0.4970233368707781,\n \"rmse_model_h3\": 1.0538489260016293,\n \"mae_model_h3\": 0.7471097183497802,\n \"rel_improvement_rmse_h1\": 0.4458427959311808,\n \"dm_stat_h1\": 2.302120035797033,\n \"dm_pvalue_h1\": 0.021328403819426134,\n \"conclusion_code_h1\": \"model_better_significant\",\n \"rel_improvement_rmse_h3\": 0.25787396930332507,\n \"dm_stat_h3\": 1.8644851017183233,\n \"dm_pvalue_h3\": 0.06225361183400402,\n \"conclusion_code_h3\": \"model_better_not_significant\",\n \"mean_abs_revision_h1_targets\": 0.5482198028388884,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"n_input_rows\": {\n  \"first_release.csv\": 28,\n  \"vintages/RSAFS_vintage_2025-10-31.csv\": 404,\n  \"vintages/PAYEMS_vintage_2025-10-31.csv\": 1040,\n  \"vintages/RSAFS_vintage_2025-11-30.csv\": 405,\n  \"vintages/PAYEMS_vintage_2025-11-30.csv\": 1041,\n  \"vintages/RSAFS_vintage_2025-12-31.csv\": 406,\n  \"vintages/PAYEMS_vintage_2025-12-31.csv\": 1043,\n  \"vintages/RSAFS_vintage_2026-01-31.csv\": 407,\n  \"vintages/PAYEMS_vintage_2026-01-31.csv\": 1044,\n  \"vintages/RSAFS_vintage_2026-02-28.csv\": 408,\n  \"vintages/PAYEMS_vintage_2026-02-28.csv\": 1045,\n  \"vintages/RSAFS_vintage_2026-03-31.csv\": 409,\n  \"vintages/PAYEMS_vintage_2026-03-31.csv\": 1046,\n  \"vintages/RSAFS_vintage_2026-04-30.csv\": 411,\n  \"vintages/PAYEMS_vintage_2026-04-30.csv\": 1047,\n  \"vintages/RSAFS_vintage_2026-05-31.csv\": 412,\n  \"vintages/PAYEMS_vintage_2026-05-31.csv\": 1048,\n  \"vintages/RSAFS_vintage_2026-06-30.csv\": 413,\n  \"vintages/PAYEMS_vintage_2026-06-30.csv\": 1049,\n  \"vintages/RSAFS_vintage_2026-07-31.csv\": 414,\n  \"vintages/PAYEMS_vintage_2026-07-31.csv\": 1050,\n  \"RSAFS_current.csv\": 417,\n  \"UMCSENT.csv\": 887\n },\n \"n_analysed\": 20,\n \"random_seed\": 20261030\n}\n{\n  \"publication_audit\": [\n    {\n      \"origin\": \"2025-10-31\",\n      \"last_month\": \"2025-08-01\",\n      \"months_behind\": 2,\n      \"payems_last\": \"2025-08-01\"\n    },\n    {\n      \"origin\": \"2025-11-30\",\n      \"last_month\": \"2025-09-01\",\n      \"months_behind\": 2,\n      \"payems_last\": \"2025-09-01\"\n    },\n    {\n      \"origin\": \"2025-12-31\",\n      \"last_month\": \"2025-10-01\",\n      \"months_behind\": 2,\n      \"payems_last\": \"2025-11-01\"\n    },\n    {\n      \"origin\": \"2026-01-31\",\n      \"last_month\": \"2025-11-01\",\n      \"months_behind\": 2,\n      \"payems_last\": \"2025-12-01\"\n    },\n    {\n      \"origin\": \"2026-02-28\",\n      \"last_month\": \"2025-12-01\",\n      \"months_behind\": 2,\n      \"payems_last\": \"2026-01-01\"\n    },\n    {\n      \"origin\": \"2026-03-31\",\n      \"last_month\": \"2026-01-01\",\n      \"months_behind\": 2,\n      \"payems_last\": \"2026-02-01\"\n    },\n    {\n      \"origin\": \"2026-04-30\",\n      \"last_month\": \"2026-03-01\",\n      \"months_behind\": 1,\n      \"payems_last\": \"2026-03-01\"\n    },\n    {\n      \"origin\": \"2026-05-31\",\n      \"last_month\": \"2026-04-01\",\n      \"months_behind\": 1,\n      \"payems_last\": \"2026-04-01\"\n    },\n    {\n      \"origin\": \"2026-06-30\",\n      \"last_month\": \"2026-05-01\",\n      \"months_behind\": 1,\n      \"payems_last\": \"2026-05-01\"\n    },\n    {\n      \"origin\": \"2026-07-31\",\n      \"last_month\": \"2026-06-01\",\n      \"months_behind\": 1,\n      \"payems_last\": \"2026-06-01\"\n    }\n  ],\n  \"bootstrap\": {\n    \"1\": {\n      \"rmse_diff_naive_minus_model\": 0.44444924822569176,\n      \"ci95\": [\n        0.2605365693579822,\n        0.6456694555820662\n      ],\n      \"share_positive\": 1.0\n    },\n    \"3\": {\n      \"rmse_diff_naive_minus_model\": 0.36619144775041756,\n      \"ci95\": [\n        -0.029850618374571158,\n        0.9056331601066206\n      ],\n      \"share_positive\": 0.9604960496049605\n    }\n  },\n  \"revisions_h1\": [\n    [\n      \"2025-09-01\",\n      3.62332320745983,\n      4.17354,\n      -0.5502167925401702\n    ],\n    [\n      \"2025-10-01\",\n      2.7460176832785423,\n      3.410194,\n      -0.6641763167214578\n    ],\n    [\n      \"2025-11-01\",\n      2.83868101772185,\n      3.277601,\n      -0.4389199822781502\n    ],\n    [\n   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-p /Users/simon-pierreboucher/wp29/runs/RT0012/out; cat > /Users/simon-pierreboucher/wp29/runs/RT0012/out/analysis.py <<'EOF'\n\"\"\"T1-forecast: real-time forecasting of US retail-sales YoY growth from ALFRED vintages.\nRun from workspace root:  ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nDATA, OUT = \"data\", \"out\"\nORIGINS = [\"2025-10-31\", \"2025-11-30\", \"2025-12-31\", \"2026-01-31\", \"2026-02-28\",\n           \"2026-03-31\", \"2026-04-30\", \"2026-05-31\", \"2026-06-30\", \"2026-07-31\"]\nHORIZONS = [1, 3]\nDRIFT_WINDOW = 36  # months of m/m log changes used for the drift (fixed a priori)\nSEED = 20261030\nn_rows = {}\n\ndef read(path):\n    d = pd.read_csv(os.path.join(DATA, path), parse_dates=[\"observation_date\"])\n    n_rows[path] = int(len(d))\n    s = d.set_index(\"observation_date\").iloc[:, 0].astype(float)\n    s.index.freq = None\n    return s\n\ndef yoy(level):\n    return 100 * np.log(level / level.shift(12))\n\nfr = pd.read_csv(os.path.join(DATA, \"first_release.csv\"), parse_dates=[\"target_month\"])\nn_rows[\"first_release.csv\"] = int(len(fr))\nactual = fr.set_index(\"target_month\")[\"yoy_first_release\"]\n\ndef add_months(ts, k):\n    return ts + pd.DateOffset(months=k)\n\nrows, audit = [], []\nfor V in ORIGINS:\n    L = read(f\"vintages/RSAFS_vintage_{V}.csv\")\n    P = read(f\"vintages/PAYEMS_vintage_{V}.csv\")  # read for audit; not used by the model\n    assert L.isna().sum() == 0\n    g = yoy(L)\n    last = L.index[-1]\n    vdate = pd.Timestamp(V)\n    lag = (vdate.year - last.year) * 12 + vdate.month - last.month\n    audit.append({\"origin\": V, \"last_month\": str(last.date()), \"months_behind\": int(lag),\n                  \"payems_last\": str(P.index[-1].date())})\n    # AR(1) on g, pairs with 1993-02 <= m <= last\n    gg = g.dropna()\n    y = gg.loc[\"1993-02-01\":last]\n    x = gg.shift(1).loc[y.index]\n    X = np.column_stack([np.ones(len(y)), x.values])\n    beta = np.linalg.lstsq(X, y.values, rcond=None)[0]\n    # model: drift-in-levels with exact base effects\n    dl = 100 * np.log(L).diff().dropna()\n    mu = dl.iloc[-DRIFT_WINDOW:].mean()\n    for h in HORIZONS:\n        T = add_months(last, h)\n        f_naive = g.loc[last]\n        f_snaive = g.loc[add_months(T, -12)]\n        f = g.loc[last]\n        for _ in range(h):\n            f = beta[0] + beta[1] * f\n        f_ar1 = f\n        # g_T = 100 ln(L_last/L_{T-12}) + h*mu  (L_{T-12} is in the vintage)\n        f_model = 100 * np.log(L.loc[last] / L.loc[add_months(T, -12)]) + h * mu\n        for name, val in [(\"naive\", f_naive), (\"snaive\", f_snaive), (\"ar1\", f_ar1), (\"model\", f_model)]:\n            rows.append({\"origin_date\": V, \"target_date\": T.strftime(\"%Y-%m-%d\"), \"horizon\": h,\n                         \"forecast\": float(val), \"actual_first_release\": float(actual.loc[T]),\n                         \"model\": name, \"vintage_used\": V})\n\nfc = pd.DataFrame(rows)\nfc[\"error\"] = fc[\"actual_first_release\"] - fc[\"forecast\"]\nfc.drop(columns=\"error\").to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6f\")\n\ntab = []\nfor m in [\"naive\", \"snaive\", \"ar1\", \"model\"]:\n    for h in HORIZONS:\n        e = fc[(fc.model == m) & (fc.horizon == h)][\"error\"].values\n        tab.append({\"model\": m, \"horizon\": h, \"rmse\": float(np.sqrt(np.mean(e**2))),\n                    \"mae\": float(np.mean(np.abs(e))), \"n\": int(len(e))})\ntab = pd.DataFrame(tab)\ntab.to_csv(os.path.join(OUT, \"table1.csv\"), index=False, float_format=\"%.6f\")\n\ndef dm(e_naive, e_model, h):\n    d = e_naive**2 - e_model**2\n    n = len(d); dbar = d.mean(); dc = d - dbar\n    lrv = np.sum(dc**2) / n\n    for j in range(1, h):\n        w = 1 - j / h\n        lrv += 2 * w * np.sum(dc[j:] * dc[:-j]) / n\n    stat = dbar / np.sqrt(lrv / n)\n    return float(stat), float(2 * (1 - stats.norm.cdf(abs(stat))))\n\nres = {\"task_id\": \"T1-forecast\",\n       \"model_name\": \"Drift-in-levels with exact base effects (RW-drift36)\",\n       \"model_description\": (\n           \"The YoY growth of target month T = last(V)+h is written exactly as \"\n           \"g_T = 100*ln(L_last/L_{T-12}) + sum_{k=1..h} 100*dln L_{last+k}. The first term uses only \"\n           \"levels published in the RSAFS vintage dated V (L_{T-12} is always in the vintage), so the \"\n           \"'base effect' of months dropping out of the 12-month window is known exactly at the origin. \"\n           \"The unknown future monthly log changes are each forecast by their mean over the last 36 \"\n           \"monthly log changes available in the same vintage (a random walk with drift in log levels; \"\n           \"window fixed a priori, not tuned on evaluation targets). Re-estimated at every origin from the \"\n           \"vintage dated V only; PAYEMS and UMCSENT are not used (no information leakage possible); \"\n           \"RSAFS_current is not used.\"),\n       \"n_origins\": len(ORIGINS), \"first_origin\": ORIGINS[0], \"last_origin\": ORIGINS[-1]}\nfor _, r in tab.iterrows():\n    res[f\"rmse_{r.model}_h{r.horizon}\"] = r.rmse\n    res[f\"mae_{r.model}_h{r.horizon}\"] = r.mae\n\nrng = np.random.default_rng(SEED)\nboot = {}\nfor h in HORIZONS:\n    en = fc[(fc.model == \"naive\") & (fc.horizon == h)].sort_values(\"origin_date\")[\"error\"].values\n    em = fc[(fc.model == \"model\") & (fc.horizon == h)].sort_values(\"origin_date\")[\"error\"].values\n    res[f\"rel_improvement_rmse_h{h}\"] = float(1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"])\n    s, p = dm(en, em, h)\n    res[f\"dm_stat_h{h}\"], res[f\"dm_pvalue_h{h}\"] = s, p\n    better = res[f\"rmse_model_h{h}\"] < res[f\"rmse_naive_h{h}\"]\n    sig = p < 0.05\n    res[f\"conclusion_code_h{h}\"] = (\"model_better_\" if better else \"naive_better_\") + (\"significant\" if sig else \"not_significant\")\n    # circular moving-block bootstrap (block length max(h,2)) of RMSE_naive - RMSE_model\n    n = len(en); b = max(h, 2); B = 9999; diffs = np.empty(B)\n    for i in range(B):\n        starts = rng.integers(0, n, int(np.ceil(n / b)))\n        idx = np.concatenate([(np.arange(s0, s0 + b) % n) for s0 in starts])[:n]\n        diffs[i] = np.sqrt(np.mean(en[idx]**2)) - np.sqrt(np.mean(em[idx]**2))\n    boot[h] = {\"rmse_diff_naive_minus_model\": float(np.sqrt(np.mean(en**2)) - np.sqrt(np.mean(em**2))),\n               \"ci95\": [float(np.percentile(diffs, 2.5)), float(np.percentile(diffs, 97.5))],\n               \"share_positive\": float(np.mean(diffs > 0))}\n\n# (d) revisions\ncur = read(\"RSAFS_current.csv\")\ngcur = yoy(cur)\nt1 = fc[(fc.model == \"naive\") & (fc.horizon == 1)][\"target_date\"].map(pd.Timestamp)\nrevs = [(str(T.date()), float(gcur.loc[T]), float(actual.loc[T]), float(gcur.loc[T] - actual.loc[T])) for T in t1]\nres[\"mean_abs_revision_h1_targets\"] = float(np.mean([abs(r[3]) for r in revs]))\n\num = read(\"UMCSENT.csv\")  # read for audit only; not used by the model\nres[\"feasibility\"] = \"complete\"\nres[\"feasibility_reason\"] = None\nlag_txt = \"; \".join(f\"{a['origin']}->last {a['last_month'][:7]} ({a['months_behind']}m)\" for a in audit)\nres[\"data_issues\"] = [\n    {\"issue\": \"Irregular RSAFS publication timing: last(V) is not always the month before V. \" + lag_txt,\n     \"handling\": \"Targets defined relative to last(V) as required (T = last(V)+h); origins 2025-10-31 and 2026-03-31 forecast from a vintage two months behind, so their h=1 target is the month that is normally already published.\"},\n    {\"issue\": \"Two target months (2026-02, 2026-03) were first released in the same month-end vintage (2026-04-30); 2025-09 first appeared only in the 2025-11-30 vintage (publication disruption).\",\n     \"handling\": \"Actuals taken as given from first_release.csv (first month-end appearance); no adjustment.\"},\n    {\"issue\": \"Annual/benchmark revision of RSAFS between the 2026-08-31 and 2026-09-30 vintages lowered the level of the whole series by roughly 4% (e.g. 2025-08: 731700 in the 2026-07-31 vintage vs 700407 in the current file); first_release levels for 2026-08 onward are on the new basis.\",\n     \"handling\": \"All comparisons use log YoY growth computed within a single vintage, which is largely invariant to proportional level rebasing; documented as affecting estimand (d).\"},\n    {\"issue\": \"PAYEMS vintage 2026-02-28 contains the annual benchmark revision (level about 1 million lower than in the 2026-01-31 vintage).\",\n     \"handling\": \"PAYEMS not used by the model; noted only.\"},\n    {\"issue\": \"UMCSENT has 210 empty values, all before 1978 (quarterly/irregular survey period).\",\n     \"handling\": \"UMCSENT not used by the model; no imputation needed.\"},\n    {\"issue\": \"RSAFS 2026-07 first release (763602, old basis) vs current value 729915 (new basis): level comparisons across vintages are not meaningful.\",\n     \"handling\": \"Revision measure (d) computed on growth rates only.\"},\n]\nres[\"n_input_rows\"] = n_rows\nres[\"n_analysed\"] = int(len(fc[fc.model == \"model\"]))\nres[\"random_seed\"] = SEED\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\nwith open(os.path.join(OUT, \"supplement.json\"), \"w\") as f:\n    json.dump({\"publication_audit\": audit, \"bootstrap\": boot, \"revisions_h1\": revs,\n               \"ar1_note\": \"see forecasts.csv\"}, f, indent=2)\n\nfig, axes = plt.subplots(2, 1, figsize=(8, 7), sharex=True)\nfor ax, h in zip(axes, HORIZONS):\n    sub = fc[fc.horizon == h]\n    a = sub[sub.model == \"naive\"].sort_values(\"target_date\")\n    t = pd.to_datetime(a.target_date)\n    ax.plot(t, a.actual_first_release, \"k-o\", label=\"first-release actual\")\n    for m, st in [(\"naive\", \"C0--s\"), (\"ar1\", \"C2:^\"), (\"model\", \"C3-d\")]:\n        s2 = sub[sub.model == m].sort_values(\"target_date\")\n        ax.plot(pd.to_datetime(s2.target_date), s2.forecast, st, label=m)\n    ax.set_title(f\"h = {h}\"); ax.set_ylabel(\"YoY growth, %\"); ax.grid(alpha=.3)\naxes[0].legend(fontsize=8)\nfig.suptitle(\"US retail & food services sales YoY growth: real-time forecasts vs first release\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=130)\nprint(json.dumps({k: v for k, v in res.items() if k not in (\"data_issues\", \"model_description\")}, indent=1))\nEOF\ncd /Users/simon-pierreboucher/wp29/runs/RT0012 && ~/wp29/venv/bin/python out/analysis.py && cat out/supplement.json && cat out/forecasts.csv"}}]}}
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{"parentUuid":"980119a8-e5aa-40bd-9553-0e57661504b8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXssUacTtL1pM7y9oDLc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QayxW7Wd6R3YPB5qZtd5LT","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\np='out/analysis.py'; s=open(p).read()\ns=s.replace('''lag_txt = \"; \".join(f\"{a['origin']}->last {a['last_month'][:7]} ({a['months_behind']}m)\" for a in audit)''',\n'''lag_txt = \"; \".join(f\"{a['origin']}->last {a['last_month'][:7]}\" for a in audit if a['months_behind'] > 1)''')\ns=s.replace('''\"Irregular RSAFS publication timing: last(V) is not always the month before V. \" + lag_txt,''',\n'''\"Irregular RSAFS publication timing (normal: the month-end vintage ends one calendar month before V). Delayed origins (vintage ends two months before V): \" + lag_txt + \". The 2026-04-30 vintage added two months (2026-02 and 2026-03) at once; from then on timing is normal.\",''')\ns=s.replace('''\"handling\": \"Targets defined relative to last(V) as required (T = last(V)+h); origins 2025-10-31 and 2026-03-31 forecast from a vintage two months behind, so their h=1 target is the month that is normally already published.\"},''',\n'''\"handling\": \"Targets defined relative to last(V) as required (T = last(V)+h), never relative to the calendar; for delayed origins the h=1 target is a month that would normally already be published. Target month 2026-03 is therefore never an h=1 target, and 2026-02/2026-04 appear as targets at both horizons from different origins.\"},''')\n# sensitivity of drift window (post hoc, reporting only)\ns=s.replace('''with open(os.path.join(OUT, \"supplement.json\"), \"w\") as f:''','''sens = {}\nfor W in [12, 24, 36, 60, 120]:\n    for h in HORIZONS:\n        errs = []\n        for V in ORIGINS:\n            L = pd.read_csv(os.path.join(DATA, f\"vintages/RSAFS_vintage_{V}.csv\"), parse_dates=[0]).set_index(\"observation_date\").iloc[:, 0]\n            last = L.index[-1]; T = add_months(last, h)\n            mu = (100 * np.log(L).diff().dropna()).iloc[-W:].mean()\n            errs.append(actual.loc[T] - (100 * np.log(L.loc[last] / L.loc[add_months(T, -12)]) + h * mu))\n        sens[f\"W{W}_h{h}_rmse\"] = float(np.sqrt(np.mean(np.square(errs))))\nwith open(os.path.join(OUT, \"supplement.json\"), \"w\") as f:''')\ns=s.replace('''\"bootstrap\": boot,''','''\"bootstrap\": boot, \"drift_window_sensitivity_posthoc\": sens,''')\nopen(p,'w').write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py >/dev/null && ~/wp29/venv/bin/python -c \"\nimport json;s=json.load(open('out/supplement.json'));print(s['drift_window_sensitivity_posthoc']);r=json.load(open('out/results.json'));print(r['data_issues'][0])\"; cat out/table1.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":11688,"cache_read_input_tokens":17469,"output_tokens":1689,"output_tokens_details":{"thinking_tokens":518},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":11688},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXssUHzPf1rALKk3zpGK","requestRef":"314d109a-666b-4384-8bae-f7a9988580ce","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"29f7db6d-948b-4b92-bdca-49290c7bc3e0","timestamp":"2026-10-30T05:35:03.319Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0012","sessionId":"c66cce91-bbf6-4244-a056-ba0f0a9450c0","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"29f7db6d-948b-4b92-bdca-49290c7bc3e0","isSidechain":false,"promptId":"ef574d05-d28e-41e2-b009-fce8ac301d89","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QayxW7Wd6R3YPB5qZtd5LT","type":"tool_result","content":"{'W12_h1_rmse': 0.5502977033274408, 'W12_h3_rmse': 1.1422197516272528, 'W24_h1_rmse': 0.5309696592940419, 'W24_h3_rmse': 0.9894188948956547, 'W36_h1_rmse': 0.5524251036350836, 'W36_h3_rmse': 1.0538489260016293, 'W60_h1_rmse': 0.5949739162142739, 'W60_h3_rmse': 1.0758464480657726, 'W120_h1_rmse': 0.5516865259647128, 'W120_h3_rmse': 0.977086908979479}\n{'issue': 'Irregular RSAFS publication timing (normal: the month-end vintage ends one calendar month before V). Delayed origins (vintage ends two months before V): 2025-10-31->last 2025-08; 2025-11-30->last 2025-09; 2025-12-31->last 2025-10; 2026-01-31->last 2025-11; 2026-02-28->last 2025-12; 2026-03-31->last 2026-01. The 2026-04-30 vintage added two months (2026-02 and 2026-03) at once; from then on timing is normal.', 'handling': 'Targets defined relative to last(V) as required (T = last(V)+h), never relative to the calendar; for delayed origins the h=1 target is a month that would normally already be published. Target month 2026-03 is therefore never an h=1 target, and 2026-02/2026-04 appear as targets at both horizons from different origins.'}\nmodel,horizon,rmse,mae,n\nnaive,1,0.996874,0.848722,10\nnaive,3,1.420040,1.200449,10\nsnaive,1,1.674543,1.306670,10\nsnaive,3,1.316079,1.065869,10\nar1,1,0.940524,0.797651,10\nar1,3,1.187118,0.952690,10\nmodel,1,0.552425,0.497023,10\nmodel,3,1.053849,0.747110,10","is_error":false}]},"uuid":"ab4afab6-42bd-4078-9002-bc2110544c5b","timestamp":"2026-10-30T05:35:04.321Z","toolUseResult":{"stdout":"{'W12_h1_rmse': 0.5502977033274408, 'W12_h3_rmse': 1.1422197516272528, 'W24_h1_rmse': 0.5309696592940419, 'W24_h3_rmse': 0.9894188948956547, 'W36_h1_rmse': 0.5524251036350836, 'W36_h3_rmse': 1.0538489260016293, 'W60_h1_rmse': 0.5949739162142739, 'W60_h3_rmse': 1.0758464480657726, 'W120_h1_rmse': 0.5516865259647128, 'W120_h3_rmse': 0.977086908979479}\n{'issue': 'Irregular RSAFS publication timing (normal: the month-end vintage ends one calendar month before V). Delayed origins (vintage ends two months before V): 2025-10-31->last 2025-08; 2025-11-30->last 2025-09; 2025-12-31->last 2025-10; 2026-01-31->last 2025-11; 2026-02-28->last 2025-12; 2026-03-31->last 2026-01. The 2026-04-30 vintage added two months (2026-02 and 2026-03) at once; from then on timing is normal.', 'handling': 'Targets defined relative to last(V) as required (T = last(V)+h), never relative to the calendar; for delayed origins the h=1 target is a month that would normally already be published. Target month 2026-03 is therefore never an h=1 target, and 2026-02/2026-04 appear as targets at both horizons from different origins.'}\nmodel,horizon,rmse,mae,n\nnaive,1,0.996874,0.848722,10\nnaive,3,1.420040,1.200449,10\nsnaive,1,1.674543,1.306670,10\nsnaive,3,1.316079,1.065869,10\nar1,1,0.940524,0.797651,10\nar1,3,1.187118,0.952690,10\nmodel,1,0.552425,0.497023,10\nmodel,3,1.053849,0.747110,10","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"29f7db6d-948b-4b92-bdca-49290c7bc3e0","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0012","sessionId":"c66cce91-bbf6-4244-a056-ba0f0a9450c0","version":"2.1.314","gitBranch":"HEAD"}
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And does a simple model of my own beat the naive (no-change) forecast when errors are measured against the **first-release** value of each target month?\n\n## Data and exclusions\n- **Real-time design.** The forecast at origin V uses only `RSAFS_vintage_V.csv` (column `vintage_used` in `forecasts.csv` always equals the origin). The target is T = last(V)+h, where last(V) is the last month in that vintage. The actual is `yoy_first_release` from `first_release.csv`. My model uses no PAYEMS or UMCSENT data. Those files were read only for the data audit, so they cannot leak information. `RSAFS_current.csv` is used only for the revision estimand (d).\n- **Publication delays.** Normally a month-end vintage ends one calendar month before V. For the six origins from 2025-10-31 to 2026-03-31, the vintage ends **two** months before V: last(V) = 2025-08, 2025-09, 2025-10, 2025-11, 2025-12 and 2026-01. This matches the disrupted publication calendar of late 2025. The 2026-04-30 vintage added 2026-02 and 2026-03 at once, and the timing was normal from then on (last(V) = 2026-03, 04, 05, 06). The README mentions only 2025-10-31 and 2026-03-31 as two months behind. My audit of the files shows that all six origins are. As a result, at the delayed origins the h=1 target is a month that would normally already have been published. Target 2026-03 is never an h=1 target, and 2026-05 is never an h=3 target. Two target months (2026-02 and 2026-03) share the same first-release vintage (2026-04-30).\n- **Revisions.** Between the 2026-08-31 and 2026-09-30 vintages there was an annual/benchmark revision that lowered RSAFS levels by about 4% (e.g. 2025-08: 731,700 in the 2026-07-31 vintage vs 700,407 in the current file). Every growth rate is computed within a single vintage, so this is mostly a level rebasing, but it does show up in estimand (d). The PAYEMS 2026-02-28 vintage contains the BLS benchmark revision (about −1 million jobs); PAYEMS is unused.\n- **Missing values.** No vintage file has missing or duplicated months. UMCSENT has 210 empty values, all before 1978 (unused).\n- Nothing was excluded. All 10 origins × 2 horizons × 4 methods = 80 forecasts were evaluated.\n\n## Method\n- **Baselines** (from the vintage dated V):\n  - naive: g_last;\n  - seasonal naive: g_{T−12};\n  - AR(1): OLS of g_m on a constant and g_{m−1} for 1993-02 ≤ m ≤ last(V), iterated h steps.\n- **My model: random walk with drift in log levels, plus exact base effects (\"RW-drift36\").** The identity g_T = 100·ln(L_last/L_{T−12}) + Σ_{k=1..h} 100·Δln L_{last+k} holds exactly. Its first term is known at the origin because L_{T−12} is in the vintage, so it captures exactly the \"base effect\" of months leaving the 12-month window. That base effect is what the naive forecast ignores. Each unknown future monthly log change is forecast by the mean of the last 36 monthly log changes in the same vintage. The 36-month window was fixed before looking at any evaluation errors. The model is re-estimated at every origin.\n- **Evaluation.** RMSE and MAE against first release, with n = 10 per horizon.\n- **Diebold–Mariano test.** Model vs naive, with d_t = e²_naive − e²_model. The long-run variance uses a Bartlett kernel with truncation lag h−1, so 0 lags at h=1 and lags 1 and 2 with weights 2/3 and 1/3 at h=3. There is no HLN correction, and p-values are two-sided normal.\n- **Bootstrap.** A circular moving-block bootstrap of RMSE_naive − RMSE_model (block length max(h,2), 9,999 draws, seed 20261030).\n- **Estimand (d).** Mean of |g^current_T − yoy_first_release_T| over the 10 h=1 target months.\n\n## Results\nTable 1 (`table1.csv`; percentage points of YoY growth, n = 10 each):\n\n| model | RMSE h=1 | MAE h=1 | RMSE h=3 | MAE h=3 |\n|---|---|---|---|---|\n| naive | 0.997 | 0.849 | 1.420 | 1.200 |\n| seasonal naive | 1.675 | 1.307 | 1.316 | 1.066 |\n| AR(1) | 0.941 | 0.798 | 1.187 | 0.953 |\n| RW-drift36 (model) | **0.552** | **0.497** | **1.054** | **0.747** |\n\n- **Relative improvement over naive** (1 − RMSE_model/RMSE_naive): 0.446 at h=1 and 0.258 at h=3. The model has the lowest RMSE and MAE of all four methods at both horizons.\n- **Diebold–Mariano, model vs naive:**\n  - h=1: DM = 2.30, p = 0.021 → `model_better_significant`.\n  - h=3: DM = 1.86, p = 0.062 → `model_better_not_significant`.\n- Among the baselines, AR(1) beats naive slightly at both horizons. Seasonal naive is the worst at h=1 but beats naive at h=3.\n- `figure1.png` shows that most of the model's gain comes from anticipating base effects. Examples: the drop in YoY growth in late 2025 (targets 2025-09 to 2025-12) and the jump in 2026-04/05, which the naive forecast follows only with a lag. The model's largest misses are at h=3 for targets 2026-04 (forecast 2.24 vs actual 4.76) and 2026-06 (4.96 vs 6.50), when spring 2026 sales rose unexpectedly fast.\n- **Revisions (estimand d).** The mean absolute revision between first-release and currently published YoY growth over the 10 h=1 target months is **0.548 pp**. All 10 revisions are negative: current growth is lower than first release, ranging from −0.17 to −0.98 pp. That revision is about the same size as the model's h=1 RMSE (0.552). In other words, much of the remaining h=1 forecast error is the same order as measurement noise in the \"actual\" itself.\n\n## Uncertainty\n- **Small sample.** With only 10 origins per horizon, the DM statistic rests on 10 loss differentials, and its asymptotic N(0,1) reference distribution is not reliable. Specific concerns:\n  - At h=3 the Bartlett long-run variance is estimated from 10 overlapping, serially correlated errors and is itself very noisy.\n  - Without the Harvey–Leybourne–Newbold correction and Student-t critical values, the test tends to over-reject in samples this small.\n  - The h=1 result (p = 0.021) should therefore be read as suggestive, not decisive. With the HLN factor and t(9) critical values it would probably sit near the 5% boundary. I did not compute this; it is a qualitative statement.\n- **The h=3 result does not reach the 5% level.**\n- **Bootstrap.** The block bootstrap agrees with this picture:\n  - h=1: 95% interval for RMSE_naive − RMSE_model is [0.26, 0.65], and 100% of draws are positive.\n  - h=3: interval [−0.03, 0.91], with 96% of draws positive.\n  - With n = 10 and blocks of length 2–3, these intervals are also only rough.\n- **Sensitivity to the drift window (post hoc).** Computed after the main result and not used to choose the model, so it is reported in `supplement.json` for transparency only. RMSE at h=1/h=3 by window: 12 months 0.550/1.142; 24 months 0.531/0.989; 60 months 0.595/1.076; 120 months 0.552/0.977. Every window beats naive at both horizons. The conclusion therefore does not depend on the a-priori choice of 36 months.\n- **Regime.** The evaluation covers one 10-month period with an unusual publication calendar and a spring-2026 surge in sales. It should not be generalised to other periods without more origins.\n\n## Limitations and open issues\n- Ten origins is very few. The h=3 forecast errors overlap, so the effective sample is even smaller.\n- \"First release\" is defined at month-end granularity (from `first_release.csv`). For months whose publication was delayed, this is the first value published by the first month-end at which the month existed.\n- The current-vintage comparison (d) mixes ordinary monthly revisions with an annual revision that rebased levels. I did not separate the two.\n- PAYEMS and UMCSENT were not used. Given how little evaluation data there is, I preferred a parameter-light model that cannot overfit, and there was no out-of-sample room to test extra predictors credibly. Whether they add information remains an open question.\n- The model treats future monthly changes as equal to a historical mean. It cannot anticipate turning points such as the 2026 spring acceleration.\n- Supplementary outputs (publication audit, bootstrap, per-month revisions, window sensitivity) are in `supplement.json`, which `analysis.py` regenerates.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The drift-in-levels model with exact base effects has lower RMSE than the naive forecast at h=1 (0.552 vs 0.997; relative improvement 0.446), and the DM test rejects equal accuracy at 5% (DM=2.30, p=0.021).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rel_improvement_rmse_h1\",\"dm_stat_h1\",\"dm_pvalue_h1\",\"conclusion_code_h1\"],\"status\":\"supported\",\"caveat\":\"Only 10 origins; asymptotic DM without HLN correction may over-reject.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the model also has lower RMSE than naive (1.054 vs 1.420; improvement 0.258), but the difference is not significant at 5% (DM=1.86, p=0.062).\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"rel_improvement_rmse_h3\",\"dm_stat_h3\",\"dm_pvalue_h3\",\"conclusion_code_h3\"],\"status\":\"supported\",\"caveat\":\"Overlapping 3-step errors; long-run variance estimated from 10 observations.\"},\n {\"id\":\"C3\",\"text\":\"The model has the lowest RMSE and MAE of all four methods at both horizons; AR(1) beats naive modestly; seasonal naive is worst at h=1.\",\"results_keys\":[\"rmse_ar1_h1\",\"rmse_ar1_h3\",\"rmse_snaive_h1\",\"rmse_snaive_h3\",\"mae_model_h1\",\"mae_model_h3\",\"mae_naive_h1\",\"mae_naive_h3\",\"mae_ar1_h1\",\"mae_ar1_h3\",\"mae_snaive_h1\",\"mae_snaive_h3\"],\"status\":\"supported\",\"caveat\":\"Single short evaluation period.\"},\n {\"id\":\"C4\",\"text\":\"The mean absolute revision between first-release and current YoY growth of the h=1 target months is 0.548 pp, similar in size to the model's h=1 RMSE; all ten revisions are downward.\",\"results_keys\":[\"mean_abs_revision_h1_targets\"],\"status\":\"supported\",\"caveat\":\"The direction of revisions is reported in supplement.json; includes an annual revision that rebased levels.\"},\n {\"id\":\"C5\",\"text\":\"Six of the ten origins (2025-10-31 to 2026-03-31) used vintages ending two months before the origin date, so targets were defined relative to last(V), not the calendar.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"The README mentions only two of these delayed origins explicitly.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('claims.json'));print('ok')\"; ls"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1957,"cache_read_input_tokens":31496,"output_tokens":4341,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1957},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsuBq6f3PVnyFLVrczK","requestRef":"9590810f-66e1-46be-91b5-b5f7d551631f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"142829ed-679a-4fe4-9f60-9450eba30dc7","timestamp":"2026-10-30T05:35:50.392Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0012","sessionId":"c66cce91-bbf6-4244-a056-ba0f0a9450c0","version":"2.1.314","gitBranch":"HEAD"}
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{"type":"api-request-blob","sessionId":"c66cce91-bbf6-4244-a056-ba0f0a9450c0","hash":"c4d6b35e3ebad70677cf705228c139e6aaaad6c9b4b869ae9db7a4ef11c0dbf5","message":{"role":"assistant","content":[{"type":"text","text":"Now the report and claims."},{"type":"tool_use","id":"toolu_01NNiPDuGXDXNumnA79dGbfv","name":"Bash","input":{"command":"cd out; cat > report.md <<'EOF'\n# Real-time forecasting of US retail-sales YoY growth from ALFRED vintages (T1-forecast)\n\n## Question\nUsing only the data published at each of 10 month-end forecast origins (2025-10-31 … 2026-07-31), how accurately could the year-over-year growth of US retail and food services sales (RSAFS), g_m = 100·ln(L_m/L_{m−12}), have been forecast 1 and 3 months past the last published month? And does a simple model of my own beat the naive (no-change) forecast when errors are measured against the **first-release** value of each target month?\n\n## Data and exclusions\n- **Real-time design.** The forecast at origin V uses only `RSAFS_vintage_V.csv` (column `vintage_used` in `forecasts.csv` always equals the origin). The target is T = last(V)+h, where last(V) is the last month in that vintage. The actual is `yoy_first_release` from `first_release.csv`. My model uses no PAYEMS or UMCSENT data. Those files were read only for the data audit, so they cannot leak information. `RSAFS_current.csv` is used only for the revision estimand (d).\n- **Publication delays.** Normally a month-end vintage ends one calendar month before V. For the six origins from 2025-10-31 to 2026-03-31, the vintage ends **two** months before V: last(V) = 2025-08, 2025-09, 2025-10, 2025-11, 2025-12 and 2026-01. This matches the disrupted publication calendar of late 2025. The 2026-04-30 vintage added 2026-02 and 2026-03 at once, and the timing was normal from then on (last(V) = 2026-03, 04, 05, 06). The README mentions only 2025-10-31 and 2026-03-31 as two months behind. My audit of the files shows that all six origins are. As a result, at the delayed origins the h=1 target is a month that would normally already have been published. Target 2026-03 is never an h=1 target, and 2026-05 is never an h=3 target. Two target months (2026-02 and 2026-03) share the same first-release vintage (2026-04-30).\n- **Revisions.** Between the 2026-08-31 and 2026-09-30 vintages there was an annual/benchmark revision that lowered RSAFS levels by about 4% (e.g. 2025-08: 731,700 in the 2026-07-31 vintage vs 700,407 in the current file). Every growth rate is computed within a single vintage, so this is mostly a level rebasing, but it does show up in estimand (d). The PAYEMS 2026-02-28 vintage contains the BLS benchmark revision (about −1 million jobs); PAYEMS is unused.\n- **Missing values.** No vintage file has missing or duplicated months. UMCSENT has 210 empty values, all before 1978 (unused).\n- Nothing was excluded. All 10 origins × 2 horizons × 4 methods = 80 forecasts were evaluated.\n\n## Method\n- **Baselines** (from the vintage dated V):\n  - naive: g_last;\n  - seasonal naive: g_{T−12};\n  - AR(1): OLS of g_m on a constant and g_{m−1} for 1993-02 ≤ m ≤ last(V), iterated h steps.\n- **My model: random walk with drift in log levels, plus exact base effects (\"RW-drift36\").** The identity g_T = 100·ln(L_last/L_{T−12}) + Σ_{k=1..h} 100·Δln L_{last+k} holds exactly. Its first term is known at the origin because L_{T−12} is in the vintage, so it captures exactly the \"base effect\" of months leaving the 12-month window. That base effect is what the naive forecast ignores. Each unknown future monthly log change is forecast by the mean of the last 36 monthly log changes in the same vintage. The 36-month window was fixed before looking at any evaluation errors. The model is re-estimated at every origin.\n- **Evaluation.** RMSE and MAE against first release, with n = 10 per horizon.\n- **Diebold–Mariano test.** Model vs naive, with d_t = e²_naive − e²_model. The long-run variance uses a Bartlett kernel with truncation lag h−1, so 0 lags at h=1 and lags 1 and 2 with weights 2/3 and 1/3 at h=3. There is no HLN correction, and p-values are two-sided normal.\n- **Bootstrap.** A circular moving-block bootstrap of RMSE_naive − RMSE_model (block length max(h,2), 9,999 draws, seed 20261030).\n- **Estimand (d).** Mean of |g^current_T − yoy_first_release_T| over the 10 h=1 target months.\n\n## Results\nTable 1 (`table1.csv`; percentage points of YoY growth, n = 10 each):\n\n| model | RMSE h=1 | MAE h=1 | RMSE h=3 | MAE h=3 |\n|---|---|---|---|---|\n| naive | 0.997 | 0.849 | 1.420 | 1.200 |\n| seasonal naive | 1.675 | 1.307 | 1.316 | 1.066 |\n| AR(1) | 0.941 | 0.798 | 1.187 | 0.953 |\n| RW-drift36 (model) | **0.552** | **0.497** | **1.054** | **0.747** |\n\n- **Relative improvement over naive** (1 − RMSE_model/RMSE_naive): 0.446 at h=1 and 0.258 at h=3. The model has the lowest RMSE and MAE of all four methods at both horizons.\n- **Diebold–Mariano, model vs naive:**\n  - h=1: DM = 2.30, p = 0.021 → `model_better_significant`.\n  - h=3: DM = 1.86, p = 0.062 → `model_better_not_significant`.\n- Among the baselines, AR(1) beats naive slightly at both horizons. Seasonal naive is the worst at h=1 but beats naive at h=3.\n- `figure1.png` shows that most of the model's gain comes from anticipating base effects. Examples: the drop in YoY growth in late 2025 (targets 2025-09 to 2025-12) and the jump in 2026-04/05, which the naive forecast follows only with a lag. The model's largest misses are at h=3 for targets 2026-04 (forecast 2.24 vs actual 4.76) and 2026-06 (4.96 vs 6.50), when spring 2026 sales rose unexpectedly fast.\n- **Revisions (estimand d).** The mean absolute revision between first-release and currently published YoY growth over the 10 h=1 target months is **0.548 pp**. All 10 revisions are negative: current growth is lower than first release, ranging from −0.17 to −0.98 pp. That revision is about the same size as the model's h=1 RMSE (0.552). In other words, much of the remaining h=1 forecast error is the same order as measurement noise in the \"actual\" itself.\n\n## Uncertainty\n- **Small sample.** With only 10 origins per horizon, the DM statistic rests on 10 loss differentials, and its asymptotic N(0,1) reference distribution is not reliable. Specific concerns:\n  - At h=3 the Bartlett long-run variance is estimated from 10 overlapping, serially correlated errors and is itself very noisy.\n  - Without the Harvey–Leybourne–Newbold correction and Student-t critical values, the test tends to over-reject in samples this small.\n  - The h=1 result (p = 0.021) should therefore be read as suggestive, not decisive. With the HLN factor and t(9) critical values it would probably sit near the 5% boundary. I did not compute this; it is a qualitative statement.\n- **The h=3 result does not reach the 5% level.**\n- **Bootstrap.** The block bootstrap agrees with this picture:\n  - h=1: 95% interval for RMSE_naive − RMSE_model is [0.26, 0.65], and 100% of draws are positive.\n  - h=3: interval [−0.03, 0.91], with 96% of draws positive.\n  - With n = 10 and blocks of length 2–3, these intervals are also only rough.\n- **Sensitivity to the drift window (post hoc).** Computed after the main result and not used to choose the model, so it is reported in `supplement.json` for transparency only. RMSE at h=1/h=3 by window: 12 months 0.550/1.142; 24 months 0.531/0.989; 60 months 0.595/1.076; 120 months 0.552/0.977. Every window beats naive at both horizons. The conclusion therefore does not depend on the a-priori choice of 36 months.\n- **Regime.** The evaluation covers one 10-month period with an unusual publication calendar and a spring-2026 surge in sales. It should not be generalised to other periods without more origins.\n\n## Limitations and open issues\n- Ten origins is very few. The h=3 forecast errors overlap, so the effective sample is even smaller.\n- \"First release\" is defined at month-end granularity (from `first_release.csv`). For months whose publication was delayed, this is the first value published by the first month-end at which the month existed.\n- The current-vintage comparison (d) mixes ordinary monthly revisions with an annual revision that rebased levels. I did not separate the two.\n- PAYEMS and UMCSENT were not used. Given how little evaluation data there is, I preferred a parameter-light model that cannot overfit, and there was no out-of-sample room to test extra predictors credibly. Whether they add information remains an open question.\n- The model treats future monthly changes as equal to a historical mean. It cannot anticipate turning points such as the 2026 spring acceleration.\n- Supplementary outputs (publication audit, bootstrap, per-month revisions, window sensitivity) are in `supplement.json`, which `analysis.py` regenerates.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The drift-in-levels model with exact base effects has lower RMSE than the naive forecast at h=1 (0.552 vs 0.997; relative improvement 0.446), and the DM test rejects equal accuracy at 5% (DM=2.30, p=0.021).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rel_improvement_rmse_h1\",\"dm_stat_h1\",\"dm_pvalue_h1\",\"conclusion_code_h1\"],\"status\":\"supported\",\"caveat\":\"Only 10 origins; asymptotic DM without HLN correction may over-reject.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the model also has lower RMSE than naive (1.054 vs 1.420; improvement 0.258), but the difference is not significant at 5% (DM=1.86, p=0.062).\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"rel_improvement_rmse_h3\",\"dm_stat_h3\",\"dm_pvalue_h3\",\"conclusion_code_h3\"],\"status\":\"supported\",\"caveat\":\"Overlapping 3-step errors; long-run variance estimated from 10 observations.\"},\n {\"id\":\"C3\",\"text\":\"The model has the lowest RMSE and MAE of all four methods at both horizons; AR(1) beats naive modestly; seasonal naive is worst at h=1.\",\"results_keys\":[\"rmse_ar1_h1\",\"rmse_ar1_h3\",\"rmse_snaive_h1\",\"rmse_snaive_h3\",\"mae_model_h1\",\"mae_model_h3\",\"mae_naive_h1\",\"mae_naive_h3\",\"mae_ar1_h1\",\"mae_ar1_h3\",\"mae_snaive_h1\",\"mae_snaive_h3\"],\"status\":\"supported\",\"caveat\":\"Single short evaluation period.\"},\n {\"id\":\"C4\",\"text\":\"The mean absolute revision between first-release and current YoY growth of the h=1 target months is 0.548 pp, similar in size to the model's h=1 RMSE; all ten revisions are downward.\",\"results_keys\":[\"mean_abs_revision_h1_targets\"],\"status\":\"supported\",\"caveat\":\"The direction of revisions is reported in supplement.json; includes an annual revision that rebased levels.\"},\n {\"id\":\"C5\",\"text\":\"Six of the ten origins (2025-10-31 to 2026-03-31) used vintages ending two months before the origin date, so targets were defined relative to last(V), not the calendar.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"The README mentions only two of these delayed origins explicitly.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('claims.json'));print('ok')\"; ls"}}]}}
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{"parentUuid":"bcd511ac-2048-4585-9c9a-1f95abb9bc8a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsx4rjxV6CEC8pKadVK","type":"message","role":"assistant","content":[{"type":"text","text":"I've finished the full project and all required files are in `out/`. My model beat the naive forecast at both horizons, but the gain is statistically significant only one month ahead.\n\nThe model (\"RW-drift36\") works from an exact identity: year-over-year growth for a target month equals the log ratio of the last published level to the level a year before the target, plus the expected monthly growth in between. Both levels are already in the vintage published at the origin, so the effect of old months dropping out of the 12-month comparison is known exactly; the naive forecast ignores it. Each future monthly change is forecast as the mean of the last 36 monthly changes in that vintage. I fixed the 36-month window before looking at any results. The model uses only retail sales; payrolls and consumer sentiment were read for the data check but not used.\n\n| method | RMSE h=1 | RMSE h=3 |\n|---|---|---|\n| naive | 0.997 | 1.420 |\n| seasonal naive | 1.675 | 1.316 |\n| AR(1) | 0.941 | 1.187 |\n| my model | **0.552** | **1.054** |\n\n- **Improvement over naive:** 45% lower RMSE one month ahead and 26% three months ahead. The model also has the lowest MAE of the four methods at both horizons.\n- **Significance (Diebold–Mariano, model vs naive):** at h=1 the statistic is 2.30 (p = 0.021), significant at 5%; at h=3 it is 1.86 (p = 0.062), not significant. With only 10 origins and no small-sample correction, even the h=1 result is suggestive rather than decisive. A block bootstrap agrees: its 95% interval for the RMSE gain excludes zero at h=1 but not at h=3.\n- **Revisions:** first-release growth for the ten 1-month-ahead target months differs from the currently published figure by 0.548 percentage points on average, and every one was revised down. That is about the size of the model's 1-month-ahead error, so much of the remaining error is comparable to noise in the \"actual\" itself.\n- **Publication delays:** six of the ten origins (2025-10-31 to 2026-03-31) use vintages that end two months before the origin date, not the two the data README mentions. The 2026-04-30 vintage then added two months at once. All targets are defined from the last published month, as the task requires.\n- **Other data issues:** an annual revision lowered retail-sales levels by about 4% in the September 2026 vintage. Growth rates are computed within one vintage, so this mainly affects the revision figure.\n\n**Limitations:**\n- Ten origins in a single unusual period, so the results may not hold elsewhere.\n- The model cannot anticipate turning points; it missed the spring-2026 surge three months ahead.\n- Whether payrolls or sentiment would help remains untested.\n- A check after the main result showed every drift window from 12 to 120 months beats naive. It played no part in choosing the model.\n\nRunning `analysis.py` regenerates the outputs from `data/` deterministically; I ran it twice and got an identical `results.json`.\n\nFiles are in `out/`:\n- results.json\n- forecasts.csv\n- table1.csv\n- figure1.png\n- report.md\n- claims.json\n- analysis.py\n- supplement.json (publication check, bootstrap, revisions by month, window check)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4393,"cache_read_input_tokens":33453,"output_tokens":1116,"output_tokens_details":{"thinking_tokens":24},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":4393},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsx4asDoc1pw7FT7z1L","requestRef":"144c5389-bcbb-4a49-81ff-7676dddda40b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b5596a9b-8da9-435c-9c08-a67865b441c6","timestamp":"2026-10-30T05:36:01.394Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0012/out","sessionId":"c66cce91-bbf6-4244-a056-ba0f0a9450c0","version":"2.1.314","gitBranch":"HEAD"}
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